7dcb5610fe
Wraps the existing search.Query function. Same BM25 over brain/knowledge/ and brain/wiki/ that the HTTP /query endpoint serves. Plan note: handleCall switch replaces the single-line stub from Task 1 — no unknownToolError type to remove since Task 1 inlined the error. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
105 lines
3.4 KiB
Go
105 lines
3.4 KiB
Go
package mcp
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import (
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"context"
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"encoding/json"
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"fmt"
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"github.com/mathiasbq/hyperguild/ingestion/internal/search"
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)
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// tools returns the tool descriptors. Handler bodies for each tool are filled
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// in subsequent tasks; this file currently only provides the descriptors.
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func (s *Server) tools() []map[string]any {
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str := func(desc string) map[string]any {
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return map[string]any{"type": "string", "description": desc}
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}
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int_ := func(desc string) map[string]any {
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return map[string]any{"type": "integer", "description": desc}
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}
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schema := func(required []string, props map[string]any) json.RawMessage {
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b, _ := json.Marshal(map[string]any{
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"type": "object", "required": required, "properties": props,
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})
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return b
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}
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return []map[string]any{
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{
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"name": "brain_query",
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"description": "BM25 full-text search across brain/knowledge/ and brain/wiki/ markdown files.",
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"inputSchema": schema([]string{"query"}, map[string]any{
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"query": str("search terms"),
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"limit": int_("max results, default 5"),
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}),
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},
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{
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"name": "brain_write",
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"description": "Write a raw knowledge note to brain/knowledge/.",
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"inputSchema": schema([]string{"content"}, map[string]any{
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"content": str("markdown content"),
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"filename": str("optional filename"),
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"type": str("optional frontmatter type"),
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"domain": str("optional frontmatter domain"),
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}),
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},
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{
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"name": "brain_ingest_raw",
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"description": "Ingest pre-structured pages into the brain wiki, bypassing the LLM extraction step.",
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"inputSchema": schema([]string{"source", "pages"}, map[string]any{
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"source": str("source name"),
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"pages": map[string]any{"type": "array"},
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"dry_run": map[string]any{"type": "boolean"},
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}),
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},
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{
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"name": "brain_ingest",
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"description": "Ingest content into the brain wiki via the LLM extraction pipeline.",
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"inputSchema": schema([]string{}, map[string]any{
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"content": str("raw content; required when path is empty"),
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"source": str("source name; required when path is empty"),
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"path": str("file path; mutually exclusive with content+source"),
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"dry_run": map[string]any{"type": "boolean"},
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}),
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},
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{
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"name": "session_log",
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"description": "Append a structured entry to brain/sessions/<session_id>.jsonl.",
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"inputSchema": schema([]string{"session_id"}, map[string]any{
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"session_id": str("session identifier"),
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"skill": str("skill name"),
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"phase": str("phase within the skill"),
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"project_root": str("absolute project root"),
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"final_status": str("ok | error | skipped"),
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"file_path": str("optional file produced"),
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"model_used": str("optional model identifier"),
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"duration_ms": int_("optional duration in ms"),
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"message": str("optional free-text"),
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}),
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},
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}
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}
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type brainQueryArgs struct {
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Query string `json:"query"`
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Limit int `json:"limit,omitempty"`
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}
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func (s *Server) brainQuery(ctx context.Context, args json.RawMessage) (json.RawMessage, error) {
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var a brainQueryArgs
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if err := json.Unmarshal(args, &a); err != nil {
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return nil, fmt.Errorf("parse args: %w", err)
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}
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if a.Query == "" {
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return nil, fmt.Errorf("query is required")
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}
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if a.Limit == 0 {
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a.Limit = 5
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}
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results, err := search.Query(s.brainDir, a.Query, a.Limit)
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if err != nil {
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return nil, fmt.Errorf("search: %w", err)
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}
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return json.Marshal(map[string]any{"results": results})
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}
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