Updates a pet in the store with form data.
| 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 destructiveHint=true, readOnlyHint=false, and idempotentHint=false, and the description adds no behavioral context beyond that. For a destructive mutation, there is no disclosure of side effects or what 'form data' changes about the pet. No contradiction with annotations, but the description carries none of the burden.
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 wasted words. It is efficient, though the vagueness of 'form data' means brevity comes at the cost of informativeness.
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 destructive mutation with zero documented parameters, no mention of the required pet ID, and no usage context, the description is incomplete. The output schema mitigates return-value concerns, but an agent still lacks enough information to invoke this tool correctly.
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 schema exposes zero parameters, so the baseline is 4, but the description only gestures at 'form data' without enumerating fields (e.g., name, status) or indicating that a pet identifier is required. Since the empty schema leaves the agent with no parameter information and the description doesn't compensate, a 3 is appropriate.
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 specific verb ('Updates'), a resource ('a pet'), and a method qualifier ('with form data'). This implicitly distinguishes it from the sibling updatePet (which likely uses a JSON body), though it doesn't name the sibling explicitly.
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 given on when to choose this tool over updatePet, addPet, or other sibling tools. There are no usage conditions, exclusions, or prerequisites stated anywhere in the description.
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.