Where a document came from: the publisher's URL, our copy, and its sha256 so a reader can check they have the same bytes. Use it to cite anything.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Part of a filename or path |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It does disclose the key returned information (publisher URL, our copy, sha256) and explains the purpose of the hash. However, it does not state whether the operation is read-only, what happens when no matching document is found, or any other edge-case behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences and every phrase earns its place. It front-loads the core concept, then gives the intended use case. There is no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no output schema, the description covers the main essentials: what the tool returns and why a reader would use it. However, 'our copy' is somewhat vague, and the lack of an explicit return format or failure behavior leaves minor gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% coverage for the single required parameter, with 'name' described as 'Part of a filename or path.' The description adds no additional parameter-level guidance, so it does not improve on the schema. A baseline of 3 is appropriate because the schema already documents the parameter adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool's domain—document provenance—and names the concrete outputs (publisher URL, our copy, sha256). It does not use an explicit action verb like 'retrieve' or 'return', so the purpose is slightly implied rather than stated, and it does not contrast with any sibling tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives a clear usage directive: 'Use it to cite anything.' This tells an agent when to invoke the tool. However, it does not mention when not to use it or name alternative sibling tools such as read_first or query, so the guidance is useful but incomplete.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct concern: dataset discovery, data-grain warnings, safe budget lookups, staff counts, meeting full-text search, provenance, SQL catch-all, and query templates. The one soft boundary is that `query` could theoretically reproduce `budget_history` and `staff`, but the descriptions aggressively warn against naive SQL for those cases, which makes the specialization meaningful.
`list_datasets` and `search_meetings` follow a verb_noun pattern, but the rest mix bare nouns (`document`, `query`, `staff`), compound nouns (`budget_history`), an imperative phrase (`read_first`), and an adjective_noun (`worked_examples`). All names are readable and memorable, but no consistent syntactic convention holds across the set.
Eight tools is well within the ideal 3-15 range for a municipal archive server. Each tool earns its place: discovery, pre-query guidance, arbitrary SQL, two specialized wrappers that exist specifically to prevent costly analytical errors, full-text search over a separate corpus, provenance, and a curated set of executable examples.
The domain is well covered: dataset discovery, data pitfalls, arbitrary read-only querying, meeting document search, and citation. The catch-all `query` tool avoids most dead ends. Minor gaps remain — no full-text search over non-meeting documents like town reports and no bulk export/download dataset tool — but the 250k-row guardrail and publisher URLs partially mitigate these.