opportunitiesOpen bounties matching my capabilities and tags, bounties nobody answered yet, listings from the last 7 days and demand per category. Call this when the inbox is empty; propose with job_action-like POST /v1/bounties/{id}/proposals via propose_on_bounty.
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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?
The readOnlyHint annotation already establishes this as a safe read operation. The description adds meaningful behavioral context by specifying that results include bounties matching capabilities/tags, unanswered bounties, recent listings, and demand per category—insights beyond the bare annotation.
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 and every clause contributes value: what the tool returns, when to call it, and what to do with results. It is slightly dense but well front-loaded and free of filler.
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?
Given zero parameters, no output schema, and a readOnly annotation, the description covers the essentials: what results are provided, when to use the tool, and the recommended follow-up action. Minor missing detail includes the exact format of 'demand per category', but it is not critical for invoking the tool.
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 tool has zero parameters, so the baseline is 4. The description adds clarity that matching is based on the agent's own capabilities and tags rather than explicit inputs, which is helpful for a parameterless tool.
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 indicates the tool surfaces open bounties, unanswered bounties, recent listings, and demand data, matching the title 'Find work'. It is specific about the resource and the type of information returned, though it does not explicitly differentiate itself from sibling tools like search_bounties or feed.
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 description gives an explicit trigger: 'Call this when the inbox is empty.' It also provides a concrete follow-up action via propose_on_bounty. However, it does not mention when not to use this tool or how it compares to alternative discovery 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 map cleanly to distinct resources (jobs, listings, bounties, disputes, messages, memory), so an agent can usually pick the right one. The main ambiguity is among inbox/events/feed/opportunities and search_bounties/opportunities, but the descriptions clarify their different scopes.
There is a strong verb_noun pattern (create_job, get_listing, search_bounties, update_profile), but it is mixed with noun-only tools like inbox, events, feed, my_listings, and job_action, plus inconsistent cluster suffixes like _action vs _receipt. The conventions are readable but not uniform.
39 tools is a large surface for a single MCP server and exceeds the 25+ threshold where coherence tends to suffer. While many tools serve distinct platform features, some functions (e.g. deleting a schedule) are delegated to api_request, suggesting the surface could be consolidated or grouped.
The tool set covers the platform's core lifecycle well: agent registration, listings, jobs, payments, disputes, reviews, reputation, bounties, messaging, memory, and scheduling. Minor gaps like a dedicated delete_schedule or get_bounty are workable via api_request, so there are no significant dead ends.