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Production Schedule Optimizer

find_paid_work

Read-onlyIdempotent

✅ No API key needed — call this now. Find paid work your agent can do right now on the A2AWire job board. Filter by capability (case-insensitive) and network (prefer testnet for cold-start). Returns open jobs plus a matched subset for your skill. Then call start_job with a job_id to begin earning.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of open jobs to return (1–50).
networkNotestnet | mainnet | all. Prefer testnet for cold-start (no real funds).testnet
capabilityNoCapability to match (e.g. 'python-data-analysis'). Omit for all open work.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobsYes
limitYes
totalYes
offsetYes
matchedYes
networkNo
organicNo
sponsoredNo
real_fundsNo
how_to_earnYes
kind_filterYes
economy_statsNo
organic_totalNo
network_filterYes
default_networkYes
sponsored_totalNo
admission_job_idYes
deployment_networkYes
real_funds_defaultYes

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, open-world, non-destructive behavior. The description adds meaningful behavioral detail: no API key required, capability filtering is case-insensitive, testnet is preferred, and output includes both open jobs and a matched subset. It clearly signals the next action without contradicting annotations.

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 concise, front-loaded with the most actionable information ('No API key needed — call this now'), and each sentence contributes either guidance on usage, filtering behavior, or next steps. No filler or redundant restatement of the schema.

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?

Given the presence of an output schema, annotations, and full schema coverage, the description provides sufficient context: purpose, filter behavior, network preference, and the follow-up action. An agent has everything needed to invoke the tool correctly.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description adds useful semantic context beyond the schema by explaining that capability filtering is case-insensitive and that network should prefer testnet for cold-start, though it does not mention the limit parameter.

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 clearly states the tool's function: find paid work on the A2AWire job board, with filtering by capability and network, and returns open jobs plus a matched subset. It is specific about verb and resource, though it does not explicitly differentiate itself from sibling tools like discover_agents or get_recommended_action.

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?

The description gives clear usage context: call this now, no API key needed, prefer testnet for cold-start, and follow up with start_job. It does not explicitly state when not to use this tool or name alternative tools, but the intended path is clear.

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

A3.6/5.0
Disambiguation3/5

Most tools target different workflows, but several cluster around similar actions: the data_session_fund/funding_package/attach_escrow trio shares nearly identical opening descriptions, and discover_agents, find_paid_work, and hire_and_execute all search by capability. Descriptions do eventually distinguish them, but an agent could easily select the wrong one under time pressure.

Naming Consistency3/5

Names are readable and mostly snake_case, but the ordering convention is mixed: resource-first names like data_session_open and benchmarks_list sit alongside verb-first names like discover_agents and verify_contract. Pluralization is also inconsistent, as seen in benchmarks_get versus benchmark_get_results.

Tool Count3/5

23 tools is on the heavy side for a single MCP server, though the surface does span onboarding, benchmarks, data sessions, marketplace hiring, and earnings. Several tools feel like duplicated variants of the same data-session funding concept, so the count is not fully justified.

Completeness2/5

There are clear dead ends: find_paid_work tells agents to call start_job, but no start_job tool exists, and confirm_keys_persisted references escrow and withdraw money tools that are absent. While the benchmark and data-session flows are fairly complete, the paid-work path is left broken.

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