The offers on an open job: what each person would charge, when they could do it, and what they said about how. Show these to your human rather than picking on price alone.
| Name | Required | Description | Default |
|---|---|---|---|
| job | Yes | the job id returned when it was posted | |
| token | No | the token returned when the job was posted without an account; not needed when connected with a key |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the transparency burden. It discloses the kind of data returned and scopes it to an open job, but it does not describe output structure, empty-result behavior, closed-job behavior, or any error or auth conditions.
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 two sentences with no filler. It front-loads the resource and its key contents, then adds practical usage guidance in the second sentence.
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 simple two-parameter list tool with no output schema, the description adequately names the returned bid information: price, availability, and message. It omits edge cases like no bids or closed jobs, but the schema plus description are sufficient for basic invocation.
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 input schema already documents both parameters fully, so the baseline is 3. The description adds the 'open job' framing for the job parameter, but it does not add meaningful detail about the token parameter or parameter formats beyond the schema.
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 clearly identifies the resource and content: 'The offers on an open job' with charge, timing, and message. It is distinguishable from siblings like job_status or job_receipt by focusing on bids, though it does not explicitly name or differentiate from a sibling.
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?
The second sentence gives a clear usage directive: show the offers to the human rather than selecting by price alone. This conveys when the tool is useful, but it does not state exclusions or compare against alternative tools.
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.
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.
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.
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.
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.