lore-mcp

search_transcripts

Search transcript segments from Whisper outputs, with optional speaker filter to find specific dialogue.

Instructions

Search transcript segments from Whisper outputs

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "search_transcripts"
  }
}

Input Schema

Name	Required	Description	Default
`query`	Yes
`speaker`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed7 schema fields changedv0.8.5May 28, 2026

addedInput schema / additionalProperties
```
Added value: +false
```
removedInput schema / properties / query / description
```
Removed value: -"Search query"
```

addedInput schema / properties / speaker / anyOf

Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "type": "null"
+  }
+]

addedInput schema / properties / speaker / default
```
Added value: +null
```
removedInput schema / properties / speaker / description
```
Removed value: -"Filter by speaker (optional)"
```
removedInput schema / properties / speaker / type
```
Removed value: -"string"
```

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "result": {
+      "type": "string"
+    }
+  },
+  "required": [
+    "result"
+  ],
+  "type": "object",
+  "x-fastmcp-wrap-result": true
+}

First observedv0.7.0May 25, 2026

TDQS

C2.6/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden but only states the basic function. It fails to disclose behavioral traits like idempotency, error handling, or any side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and to the point, but it is too minimal. It could include more detail without being verbose. The single sentence structure is efficient but incomplete.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the existence of an output schema and multiple param options, the description lacks completeness. It does not mention output format, pagination, or any constraints on the search.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description adds no meaning beyond the schema. The 'speaker' parameter is not explained, and 'query' is only implied as a search term.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it searches transcript segments from Whisper outputs, differentiating it from other search tools like kb_search or search_corpora. However, it could be more precise about what constitutes a segment.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives such as multi_search or kb_search. The description lacks context on preferred use cases or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.