OpenPublica

Search meetings

search_meetings_semantic

Read-only

Find AI summaries by meaning; use search_meeting_content for stored words.

Results carry relevance_score and a summary preview. Narrow with government,
category_tag, topic or dates; get_meeting retrieves the full summary.

Input Schema

Name	Required	Description	Default
`limit`	No	Max meetings to return (1-25).
`query`	Yes	What to search for in plain language, e.g. 'short-term rental regulations' or 'police budget overtime'.
`topic`	No	Optional topic filter — a topic name from discover_meeting_vocabulary, e.g. 'Affordable Housing'. Restricts to meetings that discussed it.
`end_date`	No	Optional latest meeting date, MM-DD-YYYY.
`min_score`	No	Optional semantic relevance floor 0-1 (default 0.4). Raise to return only strong matches; lower to widen recall.
`match_type`	No	Semantic only; use search_meeting_content for keyword searches.	semantic
`start_date`	No	Optional earliest meeting date, MM-DD-YYYY.
`category_tag`	No	Optional meeting-type filter — an opaque category_tag from discover_meeting_vocabulary (not a guessable slug).
`government_id`	No	Optional account_id (from find_local_governments) to restrict the search to one government.
`government_ids`	No	Optional list of account_ids to search across several governments at once (e.g. to compare cities). Overrides government_id.

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed6 schema fields changedOct 17, 2026

removedInput schema / properties / match_type / anyOf

Removed value: -[
-  {
-    "enum": [
-      "semantic",
-      "keyword",
-      "hybrid"
-    ],
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]

addedInput schema / properties / match_type / const
```
Added value: +"semantic"
```
changedInput schema / properties / match_type / default
```
Previous value: -nullNew value: +"semantic"
```

changedInput schema / properties / match_type / description

Previous value: -"How to match: 'semantic' (default, by meaning — best for concepts/paraphrases), 'keyword' (analyzed words in titles, summaries and topics), or 'hybrid' (both, rank-fused). Neither searches literal transcript phrases."New value: +"Semantic only; use search_meeting_content for keyword searches."

addedInput schema / properties / match_type / type
```
Added value: +"string"
```

changedInput schema / properties / min_score / description

Previous value: -"Optional semantic relevance floor 0-1 (default 0.4). Raise to return only strong matches; lower to widen recall. Applies to semantic matching only — keyword (BM25) hits are not filtered by it."New value: +"Optional semantic relevance floor 0-1 (default 0.4). Raise to return only strong matches; lower to widen recall."

Changed1 schema field changedOct 9, 2026

changedInput schema / properties / match_type / description

Previous value: -"How to match: 'semantic' (default, by meaning — best for concepts/paraphrases), 'keyword' (exact terms — best for names, ordinance numbers, quoted phrases), or 'hybrid' (both, rank-fused). Use keyword/hybrid when the query has a literal string an embedding might miss."New value: +"How to match: 'semantic' (default, by meaning — best for concepts/paraphrases), 'keyword' (analyzed words in titles, summaries and topics), or 'hybrid' (both, rank-fused). Neither searches literal transcript phrases."

First observedSep 1, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already cover safety (readOnlyHint=true, destructiveHint=false, openWorldHint=true), so the bar is lower. The description adds genuinely useful behavior: results carry a relevance_score and only a preview, with get_meeting needed for the full text. It stops short of describing result ordering or pagination beyond the schema's limit.

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

Conciseness5/5

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

Two dense sentences, front-loaded with the core purpose and the disambiguating sibling name, followed by result shape and filtering/navigation hints. No filler or redundant restatement of the name.

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

Completeness4/5

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

With 10 parameters, an output schema, and full annotation coverage, the description supplies the missing routing and result-shape context an agent needs. Only minor gaps remain (ordering of results, how min_score interacts with relevance_score in practice), which the schema largely covers.

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

Parameters3/5

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

Schema description coverage is 100%, so every parameter (limit, min_score default 0.4, date formats, government_ids overriding government_id) is already documented. The description only restates the filter categories at a high level and adds no syntax or defaults beyond the schema, so the baseline 3 applies.

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

Purpose5/5

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

The description gives a specific verb+resource+mechanism ('Find AI summaries by meaning'), and immediately contrasts with the sibling that does lexical search ('use search_meeting_content for stored words'). An agent can distinguish this from search_meeting_content and search_meeting_transcript without opening any schema.

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

Usage Guidelines5/5

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

It names the alternative (search_meeting_content) and the condition selecting it (stored words vs meaning), describes how to narrow results (government, category_tag, topic, dates), and routes to get_meeting for the full summary. Both the routing decision and the follow-up path are explicit.

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

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