projectFetch deps.dev project metadata for a GitHub, GitLab, or Bitbucket repository (e.g. project_key="facebook/react"), returning scorecard scores, version count, and linked package references.
| Name | Required | Description | Default |
|---|---|---|---|
| project_key | Yes | e.g. "facebook/react" | |
| project_type | Yes | github | gitlab | bitbucket |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "project_key": "facebook/react",
+ "project_type": "github"
+ },
+ {
+ "project_key": "gitlab-org/gitlab",
+ "project_type": "gitlab"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Project metadata from deps.dev API",
+ "properties": {},
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds behavioral context by disclosing what the tool returns: scorecard scores, version count, and linked package references, and clarifies acceptable repository types. This goes beyond the annotations without contradicting them.
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, well-structured sentence that front-loads the primary action ('Fetch deps.dev project metadata'), includes a concrete example, and states the return fields. Every element earns its place with no redundant or filler content.
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 the tool's simplicity (2 params, no nested objects, output schema present) and the annotations covering safety, the description is largely complete. It explains the return contents and input format, but does not mention any limitations (e.g., public-only repos or rate limits), which could be useful but is not essential given the output schema.
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 coverage is 100%: both parameters (project_key and project_type) have descriptive values (example for project_key, enum-style hint for project_type). The description reinforces the project_key example but adds no new parameter semantics beyond what the schema already provides, so the baseline of 3 applies.
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 the specific verb 'Fetch' and names the exact resource ('deps.dev project metadata'), with clear scope ('for a GitHub, GitLab, or Bitbucket repository') and an illustrative example (project_key="facebook/react"). This distinguishes it from sibling tools like 'package' and 'dependencies' by focusing on repository-level metadata.
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 clearly implies when to use it: when you need project metadata for a hosted repository. However, it does not explicitly list alternatives or conditions where another tool should be used instead, such as 'package' for package-level metadata, so it lacks explicit exclusion guidance.
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.
Multiple tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical routers, the six polymarket tools cover similar prediction-market territory with fuzzy boundaries, and the deps.dev tools (package/version/dependencies/query/scan_dependency) all deliver dependency metadata. An agent would frequently need the lengthy descriptions to pick the right one.
Tool names mix several conventions: noun-only names (package, version, query, project, dependencies), verb_noun names (scan_dependency, validate_claim, resolve_entity), and family-prefixed names (ask_pipeworx_*, polymarket_*, pipeworx_*). There is no single consistent pattern across the set.
With 36 tools, the server is well past the 'heavy' threshold. The scope is also sprawling: general data querying, dependency lookup, memory management, subscriptions, prediction markets, claim verification, and AI-visibility scanning. Many tools could be consolidated or moved to separate servers.
The tool set is individually broad and covers many workflows, but the server's stated identity ('Deps Dev') does not match the dominant Pipeworx data surface, creating an unclear core purpose. Within the dependency sub-domain it is fairly complete, and the data-research workflows have decent coverage, but gaps like subscription updates and true deps.dev ecosystem coverage suggest the surface is improvised.