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Lamdis Exchange

by ai.lamdis

find_out

Pay somebody to go and find something out, and get a structured answer back rather than a photograph. Use for quotes, availability, opening hours, stock — anything where the deliverable is information. Each result comes back as provider, price, availability and notes, so you can act on it without parsing prose.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsNohow many results to collect, e.g. 3 quotes
whereNothe address it concerns
questionYeswhat you need found out, e.g. quotes for a new water heater
fee_minorYeswhat this is worth to you, in minor units
instructionsYeshow to go about it, e.g. call three local installers

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are provided, so the description carries the disclosure burden. It reveals the paid human-driven nature, the structured output shape (provider, price, availability, notes), and the contrast to a photograph. It does not mention asynchronous behavior or job tracking despite sibling job tools, but the core behaviors are disclosed.

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?

Three sentences, each earning its place: purpose and deliverable contrast, when to use, and result structure. It is front-loaded and has no filler or repetition.

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?

No output schema exists, but the description explains the return shape well. It covers the main use cases and the kinds of information to request. It does not explain job lifecycle or status-checking, which would be useful given sibling job tools, but parameters are fully documented and the invocation path is clear.

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 parameter descriptions fully document each field. The tool description adds useful context about the result format but does not add parameter-specific semantics beyond what the schema already provides, so the baseline of 3 applies.

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?

Description states a clear purpose: commission someone to gather real-world information and return structured answers. It lists concrete use cases (quotes, availability, opening hours, stock) and explicitly distinguishes the deliverable from a photograph, separating it from observation-style sibling tools.

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

Usage Guidelines4/5

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

Gives explicit guidance on when to use it ('Use for quotes, availability, opening hours, stock — anything where the deliverable is information'). It does not name alternatives or state when not to use it, but the context is clear enough for an agent to select it.

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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TDQS

A3.9/5.0
Disambiguation4/5

Most tools target clearly distinct actions: feasibility checking, posting physical work, gathering structured info, verifying truth, and reviewing job artifacts. The only plausible confusion is between find_out and observe_world, since both involve paying someone to investigate the physical world, though their outputs differ.

Naming Consistency3/5

All names are snake_case and readable, but the pattern is mixed: check_feasible, find_out, observe_world, and list_bids are verb-first, do_in_world is a unique verb+preposition, and job_evidence, job_receipt, and job_status are noun-first. The job_* family is internally consistent, but the set as a whole lacks a single predictable convention.

Tool Count5/5

Eight tools is well-scoped for a physical-world exchange: pre-flight feasibility, posting work, requesting information, verification, bidding, status, evidence, and receipts. Each tool earns its place and the count is neither bloated nor thin.

Completeness4/5

The core lifecycle is covered: check feasibility, post a job, review bids, track status, inspect evidence, and get a verifiable receipt. The main gaps are no explicit cancel/update job tool and no direct job listing tool, but agents can navigate the main workflows without dead ends.

Resources