Any question the other tools do not cover, as one read-only SQL statement over the archive database. SELECT or WITH only; a LIMIT is imposed if you omit one. Call read_first before computing anything and list_datasets to find table names. A query estimated to read more than 250,000 rows is refused — narrow it with a WHERE, or ask for one table at a time.
| Name | Required | Description | Default |
|---|---|---|---|
| sql | Yes | One SELECT statement. |
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 burden and discloses key behaviors: read-only constraint, automatic LIMIT insertion, and refusal of queries estimated to read over 250,000 rows. It does not describe result formatting or exact error behavior, but the main safety and execution traits are transparent.
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?
Three sentences with no filler: purpose, usage prerequisites, and safety/performance constraints are each covered. The most important scoping information is front-loaded before the parameter and table-name guidance.
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?
Given the tool's open-ended SQL nature, the description is unusually complete: it sets scope, gives prerequisites, states allowed syntax, explains the limit and refusal behavior, and tells the agent how to narrow queries. The absence of an output schema is acceptable for arbitrary SELECT results, and nothing essential for a safe first call is missing.
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 already fully describes the single sql parameter, so the baseline is 3. The description adds meaningful constraints beyond the schema: SELECT or WITH only, LIMIT imposed if omitted, and row-estimate refusal, which helps the agent construct valid SQL.
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 a catch-all read-only SQL query tool over the archive database, using a specific verb and resource. It distinguishes itself from siblings by explicitly claiming coverage of 'any question the other tools do not cover.'
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?
It provides explicit prerequisite steps: call read_first before computing anything and list_datasets to find table names. It also states when to use this tool and gives concrete query-shaping guidance such as SELECT/WITH only, narrowing with WHERE, and processing one table at a time.
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.