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Foundaree

Match people to a requirement

match_people
Read-onlyIdempotent

Rank public Foundaree profiles against a list of required skills — use when the user describes a need ("a co-founder who knows React and has sold to enterprises", "someone to rewire my kitchen in Kochi") rather than searching for a known person. Give the required skills as skills (or describe the need in q; skill terms are then derived from it and q also narrows candidates by full text). location, area, workType, category, availability and lookingFor are hard filters; the skills only score: score = matched ÷ required (0–1), where a listed skill counts 1 and a term found in the person's category or lookingFor counts ½. Someone with 3 of 5 skills still appears, below someone with 5; ties break on availability (available first), then recency. When nobody matches a single skill, the filtered candidates come back with score 0 and fallback: true instead of an empty list. Returns JSON with items (each: person, score, matchedSkills, missingSkills, profileUrl), total, requiredSkills and fallback. Each answer has two views: the text content is the JSON described above, and the structured content is a profile-card list (people, total, query). In apps that support MCP Apps (ChatGPT, Claude) the cards render as Foundaree profile cards.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFree-text description of the need, e.g. "someone with React and PostgreSQL experience". Used to derive skills when `skills` is absent, and as a full-text filter.
areaNoOptional neighbourhood filter within the city (case-insensitive substring), e.g. "Kakkanad"
limitNoMaximum number of ranked results. Defaults to 20.
skillsNoThe required skills, e.g. ["React", "PostgreSQL", "Kubernetes"]. Required unless q is given.
categoryNoOptional comma-separated trade/profession filter (OR semantics), e.g. "driver" or "carpenter,electrician"
locationNoOptional location filter (case-insensitive substring), e.g. "Bengaluru" or "Kochi"
workTypeNoOptional audience filter: local_service = blue-collar jobs, hands-on work (electricians, carpenters, drivers, cleaners, carers...); professional = white-collar jobs, office and professional work (developers, designers, accountants...)
lookingForNoOptional comma-separated looking-for filter (AND semantics), e.g. "cofounder" or "job,freelance"
availabilityNoOptional availability filter (exact match)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
totalYes
peopleYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedOutput schema / properties / people / items / properties / email
      Added value: +{
      +  "type": "string"
      +}
  2. First observed

TDQS

A5/5.0
Behavior5/5

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

The description discloses far more than the annotations: the exact scoring formula (matched ÷ required), half-credit for category/lookingFor matches, tie-breaking on availability then recency, and the `fallback: true` behavior for zero matches. It also details the two output views, adding meaningful behavioral context that annotations alone do not provide.

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

Conciseness5/5

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

The description is long, but every sentence earns its place given the tool's 9 parameters, scoring algorithm, fallback behavior, and dual output views. It is front-loaded with purpose and usage routing, then layers filters, scoring, output, and rendering details in a logical order.

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

Completeness5/5

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

For a complex ranking tool, the description is complete: it covers all parameter roles, the scoring and tie-breaking rules, the exact JSON response shape, the fallback case, and the structured-card rendering in supporting apps. Nothing an agent needs to invoke it correctly is missing.

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

Parameters5/5

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

Although schema description coverage is 100%, the description adds substantial semantics beyond the schema: it distinguishes hard filters from score-only skills, explains how `q` derives skills and narrows by full text, and clarifies that listed skills count 1 while matched terms in category/lookingFor count ½. This materially helps an agent choose and fill parameters correctly.

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

Purpose5/5

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

The description opens with a specific verb and resource — 'Rank public Foundaree profiles against a list of required skills' — and immediately distinguishes this tool from searching for a known person, which maps cleanly onto the sibling `search_people`. An agent can tell exactly what this tool does without opening the schema.

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

Usage Guidelines5/5

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

The description explicitly says 'use when the user describes a need' and contrasts it with 'rather than searching for a known person', providing both a positive trigger and an exclusion condition. It also explains the roles of filters versus scoring, giving clear guidance on how to invoke the tool correctly.

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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