Current institutional accreditation status, accreditor, and (when published by DAPIP) last action date and programmatic accreditations.
| Name | Required | Description | Default |
|---|---|---|---|
| unit_id | Yes | IPEDS UNITID. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With readOnlyHint and idempotentHint already declaring a safe read operation, the description adds meaningful conditional context: results are 'current' and include last-action/programmatic data only 'when published by DAPIP.' It also names the data source, helping agents set expectations for gaps in coverage.
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 sentence that front-loads the core concept ('current institutional accreditation status') and then lists the supplemental fields compactly. There is no filler or redundancy.
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 one-parameter read-only lookup, this is nearly complete: it names the return content, the source caveat, and the required identifier concept is in the schema. It could add a pointer to how to obtain unit_id or clarify DAPIP, but these are 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 only parameter, unit_id, is fully documented in the schema as 'IPEDS UNITID,' so the description does not need to restate it. It adds no further parameter-level detail, matching the baseline for high schema coverage.
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 names a specific resource — institutional accreditation — and the data fields returned: accreditor, last action date, and programmatic accreditations. It is clearly about accreditation, distinguishing it from sibling college_* tools by domain, though it lacks an explicit verb such as 'gets' or 'returns.'
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?
No guidance is given on when to use this tool versus college_search, college_compare, or other college_* siblings. The description only states what data it exposes; an agent must infer that it is for accreditation-status lookups by institution.
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.
Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.
The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.
290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.
For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.