Returns pet inventories by status.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| result | No | Response from the tool |
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?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds the 'by status' aggregation context but reveals no extra behavioral traits such as response shape or status values; the output schema is expected to carry that.
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?
A single front-loaded sentence with no filler or repetition. It states the operation and the distinguishing characteristic without waste.
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 no-parameter, read-only endpoint with an output schema, the description is nearly complete. The main gap is not explicitly distinguishing this from findPetsByStatus, and 'by status' is slightly ambiguous, so it is slightly below fully self-sufficient.
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 zero parameters and 100% schema description coverage (vacuously), so there is nothing to document. The description's 'by status' could be misread as a parameter, but it likely describes the returned aggregation; with no parameters, this is adequate.
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 uses a clear verb and resource: 'Returns pet inventories by status.' However, it does not differentiate from the sibling petstore-api.findPetsByStatus, whose name suggests a similar status-based lookup, so it is clear but not maximally distinctive.
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 statement implies that the tool is for retrieving pet inventories grouped by status, but it gives no explicit when-to-use guidance or mention of alternatives such as findPetsByStatus. An agent can infer the use case, but nothing tells it when not to use this tool.
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.
The tools are clearly namespaced by service (e.g., petstore-api, github-api, jsonplaceholder), which reduces cross-service confusion. Within each service, operations are generally distinct (e.g., getPetById vs updatePet). However, some overlap exists like updatePet and updatePetWithForm, and there are multiple 'get' tools across services that could be mixed up in a large set, but descriptions help.
Naming conventions are inconsistent across the set. Some tools use camelCase (github-api.getRepo), others use underscores (acme-mailer.send_email), and some are single simple verbs (echo-server.echo, memory.store). While each service follows its own style, the server as a whole lacks a unified pattern, making the naming chaotic.
With 38 tools, this server is heavily overloaded for a typical MCP scope. The tools span ten different services, indicating a broad aggregation rather than a focused purpose. This exceeds the recommended 3-15 tool range and even the 25+ threshold, making it feel like a collection of unrelated utilities.
The domain is unclear, but looking at each sub-service, most are incomplete. For example, github-api only offers read operations (no create/update/delete), jsonplaceholder has posts CRUD but only get for users, and open-weather lacks historical data. Memory and echo-server are trivial. The surface does not fully cover any single domain, leaving significant gaps for agent workflows.