Program-level outcomes (4-digit CIP code) for one school: median earnings one year after completion, median debt at completion, and award counts.
| Name | Required | Description | Default |
|---|---|---|---|
| unit_id | Yes | IPEDS UNITID. |
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?
Annotations already declare the operation read-only, idempotent, and non-destructive, so the safety profile is covered. The description adds useful semantic detail about the metrics ('one year after completion', 'at completion') but does not disclose other behavioral traits such as whether all programs are returned, how missing data is handled, or the response structure.
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, tightly packed sentence with no filler. It front-loads the resource and scope before enumerating the returned metrics, making it easy to scan and understand.
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 simple one-parameter lookup with no output schema, the description provides the essential return-value details: earnings, debt, and award counts at program level. It could be slightly clearer that the tool returns data for all programs for the given school rather than requiring a CIP-code input, but the schema plus description are sufficient for correct invocation.
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 only parameter, unit_id, is fully described in the schema as an IPEDS UNITID, so schema coverage is 100%. The description does not add additional parameter-level meaning, so the baseline score of 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 precisely identifies the resource (program-level outcomes), the granularity (4-digit CIP code), the scope (one school), and the specific measures returned (median earnings, median debt, award counts). This clearly distinguishes it from sibling tools like college_metrics or college_value_score.
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 phrase 'Program-level outcomes ... for one school' provides clear context for when to use this tool: when a caller needs earning, debt, or award-count metrics broken down by program. It does not name alternative tools or give explicit when-not-to-use guidance, but the scope and granularity are evident.
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.
Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.
Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.
294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.
The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.