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Lamdis Exchange

by ai.lamdis

job_evidence

The actual files somebody brought back for a job — photographs, video, transcripts — with where each one says it was taken and whether the challenge code was found in it. Use this when the verdict alone is not enough and you want to look at what was bought, or show it to the person you are acting for. Each file comes with a url you can fetch.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobYesthe job id returned when it was posted
tokenNothe token returned when the job was posted without an account; not needed when connected with a key

Schema Changelog

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

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries full behavioral burden. It does disclose useful output behavior: each file comes with a fetchable URL and includes location and challenge-code findings. However, it does not mention side effects, error conditions, or access requirements beyond what the schema already implies.

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 well-organized sentences: what the tool returns, when to use it, and a key detail about the response. Every sentence adds value and the most important information is front-loaded.

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?

The tool is simple with only two parameters and no output schema, so the description must cover return shape and usage context. It does explain the contents of each file and provides a URL note, but it stops short of describing the exact response structure or edge cases like missing evidence.

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 description coverage is 100%, so the schema already documents both parameters. The description does not add new parameter-level meaning, which is acceptable under the high-coverage baseline, but it also does not elaborate on how the job id or token are used.

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 clearly identifies the tool's resource: actual evidence files brought back for a job (photographs, video, transcripts), along with location and challenge-code status. It also distinguishes itself from verdict-type tools by saying 'when the verdict alone is not enough,' which separates it from siblings like job_status.

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?

Provides explicit when-to-use guidance: 'Use this when the verdict alone is not enough and you want to look at what was bought, or show it to the person you are acting for.' It does not name alternatives directly or state exclusion cases, but the context is clear enough for an agent to route 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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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