explain_books_checkFull detail on a single books health check: what it is, exactly where to look in the ledger, every innocent explanation to rule out before concluding anything, what it costs if it is real, what fixing it involves, and whether it is inside what Median does.
| Name | Required | Description | Default |
|---|---|---|---|
| check_id | Yes | Check id from list_books_checks, e.g. 'rec-processor-balance-unreconciled'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark read-only and idempotent, and the description adds rich behavioral detail: what it explains (what it is, where to look, innocent explanations, cost, fixing, Median's scope). This goes well beyond the annotations, providing concrete expectations.
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 a single, information-dense sentence that front-loads the core purpose and then enumerates the detailed aspects covered. No fluff or repetition.
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 one parameter, no output schema, and strong annotations, the description fully compensates by detailing what the agent will learn. It covers enough context for correct invocation and expectation setting.
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 covers the single parameter check_id with a clear description and example. The tool description does not add extra parameter semantics, but with 100% schema coverage, baseline 3 is appropriate.
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 states the tool provides 'full detail on a single books health check' with a specific verb and resource. It distinguishes from siblings like list_books_checks (listing) and score_books_health (scoring) by focusing on deep explanation of one check.
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 implies usage context—for understanding a specific check in depth—but does not explicitly name alternatives or exclusion criteria like 'use list_books_checks to enumerate checks'. Clear context is present, but no explicit when-not-to-use guidance.
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.
Tools mostly demand exactly one job: the list/explain/score path is clearly labeled per domain, and the START-HERE markers guide entry points. The fuzzy spots are get_conf_lefts and get_evidence_recipe (both refinement steps after list_compliance_obligations) plus list_services vs get_company_overview (they overlap on what Median does); these are distinguishable from the descritions but are the kinds of pairs that could send an agent the wrong way.
All twelve names follow the same verb_noun snake_case convention with consistent verbs: list the collections (_lists), get facts/computed refinements, explain single-item detail, and score the assessment. The pair structure (list vs explain vs proof) makes the intent predictable from the name for the first decode.
Twelve tools is right within the ideal for a domain-focused server: seven true domain tools (compliance + books) and five content/take-usational helpers. The content cluster (services, overview, pricing, blog, blog_post) is a bit tangential to a compliance/books mission, but this is a company-fronting server, so the zone works with a reason.
Both workflows close their loop: compliance has list → explain → confounders → evidence, and books has list → explain → score, with no obvious dead-end in either. Minor residuals exist (no explicit tool for showing covered versus non-covered states, other collaboration across the two domains), but an agent can work out these as of today's.