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Find Programs

find_programs
Read-only

Which programs match these words, across institutions? Program or award words (optionally one institution's UNITID) answer with each program's award, CIP, published credit figure verbatim, edition, source page and institution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityNoOptional city: programs at that city's institutions
limitNoOptional page size (default 20, at most 100)
queryYesProgram or award words, e.g. welding or nursing
stateNoOptional two-letter state: programs at that state's institutions
offsetNoOptional: the next_offset from a previous answer
unitidNoOptional: one institution's IPEDS UNITID

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / city
      Added value: +{
      +  "description": "Optional city: programs at that city's institutions",
      +  "type": "string"
      +}
    • addedInput schema / properties / state
      Added value: +{
      +  "description": "Optional two-letter state: programs at that state's institutions",
      +  "type": "string"
      +}
  2. Added

TDQS

A3.6/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With readOnlyHint already declaring a safe read, the description still adds value by enumerating the returned fields (award, CIP, verbatim credit figure, edition, source page, institution). Because no output schema exists, this return-shape disclosure is genuinely useful, though it says nothing about pagination or result ordering.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

It is compact, but the single sentence is convoluted: it opens as a question, then packs query inputs and output fields into one clause. Front-loading is present ('Which programs match these words') but the mixed structure blurs inputs versus outputs.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only search tool with no output schema, the description covers the query intent and the return fields, and the schema covers all six parameters. It is close to complete, missing only pagination behavior and ordering/ranking of matches.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so every parameter including city, state, limit and offset is already documented in the schema. The description only restates query words and the optional UNITID filter, adding no syntax or constraint detail beyond the schema, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb (match/find) and resource (programs), and adds scope ('across institutions') that separates it from institution-scoped lookups. It does not explicitly name a sibling like get_program or programs_for_institution, so an agent must infer the boundary.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the 'across institutions' framing and the note that UNITID is optional for narrowing, but there is no explicit when-to-use statement or named alternative. The agent is left to infer that broad keyword search is the entry point and institution-scoping is the fallback.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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