Get a single project's details.
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | No | CatchAll API key. Optional if provided via x-api-key header or CATCHALL_API_KEY env var. | |
| project_id | Yes | The project ID to retrieve. |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. The description only states it retrieves details but does not mention that it is read-only, what happens if the project_id is invalid (e.g., error handling), or any authorization requirements. Given the lack of behavioral context, the description is insufficient.
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 a single, concise sentence with no extraneous information. It is appropriately front-loaded with the core functionality. However, it could be slightly more structured, but overall it is efficient.
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 that an output schema exists (documenting return values) and the input schema has 100% coverage, the description is minimally adequate. However, for a simple retrieval tool, it lacks any mention of error conditions, rate limits, or optional parameters like api_key. The completeness is acceptable but nothing more.
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?
Schema description coverage is 100% as both api_key and project_id have descriptions in the schema. The tool description adds no additional meaning beyond what the schema already provides, so a baseline score of 3 is appropriate. No enrichment or clarification is offered.
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 states the action ('Get') and the resource ('a single project's details'), effectively specifying the verb and resource. It distinguishes itself from sibling tools like list_projects (which retrieves multiple) and create_project/delete_project/update_project, all of which imply different operations. However, it does not differentiate from get_project_overview, but the name and description are sufficient for purpose clarity.
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, such as get_project_overview or list_projects. There are no instructions on prerequisites, input validation, or expected context. The description is purely declarative without any usage direction.
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 have distinct purposes, but some pairs like create_dataset vs create_dataset_from_csv or pull_results vs pull_job_csv could cause confusion. However, descriptions clarify differences.
Tools follow a consistent verb_noun pattern (e.g., create_dataset, list_datasets) with minor exceptions like append_csv_to_dataset and pull_job_csv. Overall predictable.
60 tools is high for an MCP server, but the domain (web research, job processing, multiple resource types) justifies the count. Still borders on excessive.
The server offers full CRUD for datasets, entities, monitors, projects, webhooks, plus job submission, status polling, result retrieval (JSON/CSV), webhook management, and health endpoints. No obvious gaps.