Pricing statistics counted from the published pricing pages of audited cases: median first paid tier, free-tier prevalence, and which capabilities most often sit behind the paywall. Products without a public price are excluded rather than estimated; no ranking or traffic figures exist here because those cannot be verified freely.
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | Optional category, e.g. 'Micro SaaS'. Omit for the whole library. |
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 provided, the description carries the full burden of behavioral disclosure. It explains the data source (published pricing pages of audited cases), the exclusion rule (products without public price are excluded, not estimated), and the absence of ranking/traffic figures, all with reasoning. This goes well beyond a minimal description, though it does not mention freshness, update cadence, or output structure.
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?
Two tightly packed sentences with no filler. The first sentence enumerates the concrete statistical outputs, and the second provides essential limitations and exclusions. Every phrase earns its place, and the core information is front-loaded.
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 simple read-only analytics tool with one optional parameter and no output schema, the description gives enough to understand the returned aggregate metrics and the methodological scope. It could go further by explicitly describing how the category parameter interacts with the output, but the schema covers the parameter and the listed statistics imply the response fields.
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 already documents the single optional category parameter fully with an example and the omit behavior. The tool description adds no parameter-specific detail, but since schema coverage is 100%, the baseline of 3 applies; the description's methodological context is useful but not required for parameter understanding.
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 names a specific deliverable (pricing statistics) and enumerates exactly what is computed: median first paid tier, free-tier prevalence, and paywalled capabilities. It is clearly differentiated from the siblings by focusing on aggregate library-level benchmarks rather than individual case retrieval or search, and it explicitly rules out ranking/traffic data.
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 implies usage when aggregate pricing statistics are needed and clarifies the data boundary (public prices only), but it never explicitly says when to use this tool versus get_case or search_cases. No alternatives are named and no when-not-to-use conditions are stated, leaving the selection logic to inference.
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.
Each tool has a clearly distinct role: retrieving one specific case, searching across cases, and computing aggregate pricing benchmarks. There is no meaningful overlap or ambiguity between them.
get_case and search_cases follow a clean verb_noun pattern, while pricing_benchmarks is a noun phrase rather than an action-oriented name. The mixed style is minor and does not hurt readability.
Three tools is a well-scoped count for a focused read-only case-study and benchmark server. Each tool earns its place and there is no redundancy.
The tool surface covers discovery, deep retrieval, and cross-case aggregate analysis, which are the core needs for this domain. Since the underlying data is curated and read-only, the absence of create/update/delete operations is appropriate.