Request for help with media search, a repetitive task that could be automated with a tool. (reference price: $0.0100 per call)
| Name | Required | Description | Default |
|---|---|---|---|
| payload | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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?
With no annotations present, the description carries the full burden of disclosing behavioral traits. It only mentions a cost per call, but omits critical details: whether the call is idempotent, whether it creates side effects, requires authentication, or has rate limits. The phrase 'repetitive task that could be automated' is ambiguous and does not clarify the tool's own behavior.
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 short (two sentences plus a price note), but brevity is not a virtue when it sacrifices essential information. The price note is arguably irrelevant to the agent's decision. The description could be structured better by front-loading the actual action and including a clear purpose statement.
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?
Despite having a simple input schema and an output schema (unseen), the description fails to explain what the tool returns or how it integrates into a workflow. It is incomplete for an agent to use effectively, as it lacks any mention of output, error conditions, or typical usage flow.
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 has one parameter ('payload') with no description and 0% schema coverage. The tool description does not explain what the payload should contain or how it affects the request. This leaves the agent completely unable to construct a valid invocation.
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 'Request for help with media search, a repetitive task that could be automated with a tool' is vague. It does not specify what the tool actually does (e.g., create a ticket, log a request, or trigger a human workflow). The verb 'request' is present but the resource is unclear, and it fails to distinguish this tool from siblings like 'help-setting-up-an-instance' or 'research_pain_points'.
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?
No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, context, or exclusions. The description does not even hint at typical use cases, leaving the agent with no basis for selection.
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.
Many tools have vague or overlapping descriptions, such as multiple 'repetitive task that could be automated' tools that lack clear differentiation. The inclusion of meta-tools (e.g., research_pain_points, develop_tools) alongside domain-specific tools further blurs boundaries, making it hard for an agent to select the correct tool.
Tool names use a mix of hyphens (add-license-information-to-codebase) and underscores (develop_tools, check_tool_health), with no consistent pattern. Some names are verbose and descriptive, while others are terse, creating an inconsistent naming convention across the set.
At 20 tools, the count is borderline but not extreme. However, the set includes several tools that are purely descriptive of problems (e.g., ai-generated-code-debugging-overhead) or are meta-tools for the factory itself, which inflates the count without adding practical utility for end users.
The server's purpose is unclear, mixing codebase operations, documentation, gamification, and support tickets. There are obvious gaps: no tool for updating or deleting, and the meta-tools (research, develop, health) are not exposed as a coherent lifecycle. The surface feels incomplete for any single domain.