107 questions this archive can answer, each with the SQL that answers it. Every one is executed against the database on every build, so none of them is a claim. Start from the nearest one and edit it rather than writing a query from scratch.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 provided, the description carries the full burden of behavioral disclosure. It explicitly states that every example is executed against the database on every build, making them verified rather than speculative. This is a meaningful trust and quality signal, though the return format is not described.
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 compact and well-structured. The first sentence establishes content and validity, while the second gives the core usage directive. No filler or redundant information is present.
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 parameterless reference tool, the description adequately explains what is included, why it is reliable, and how to use it. It does not specify the exact output structure, but the nature of the content—questions with SQL—is sufficiently communicated.
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 tool accepts zero parameters and the input schema is empty, so there are no parameter semantics for the description to clarify. The baseline for a no-parameter tool is appropriately high.
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 as a collection of 107 pre-written, validated SQL examples for the archive. It conveys the resource's purpose and instructs the user to adapt existing examples. It does not explicitly name sibling tools, but the contrast with writing queries from scratch makes the purpose clear.
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 instruction to start from the nearest example and edit it rather than writing a query from scratch provides clear, actionable guidance for when to use this tool. It lacks explicit when-not-to-use conditions or named alternatives, but the intended usage is unambiguous.
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.