company_signals
PRO — the company's adopted stack entity by entity (services, tools, standards), each with how many companies across the cohort carry it and its radar ring.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes |
api-evangelist
TDQS
C2.6/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It does not disclose whether this is a read-only operation, any side effects, or access restrictions (the 'PRO' prefix might imply a paywall but isn't explained). Its behavior beyond returning data is opaque.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence with no fluff, but it's structured as a noun phrase rather than an action-oriented instruction. The 'PRO —' prefix is cryptic and not explanatory. It's concise but not effectively front-loaded with a clear verb.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With one param, no output schema, and no annotations, the description should provide more: what the output looks like, how the slug is used, what 'radar ring' means, and whether it's read-only. The description is too sparse to give an agent sufficient context for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description says nothing about the 'slug' parameter; with 0% schema description coverage, the description should compensate. It doesn't even hint that slug refers to a company identifier. The only clue is the tool name 'company_signals'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific resource ('the company's adopted stack entity by entity') and what it provides (adoption counts and radar ring). It distinguishes from siblings like find_tools or find_services by focusing on the company-level stack with cohort adoption metrics. However, it's phrased as a noun phrase rather than a verb phrase, so it's not instantly obvious it's a retrieval tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this versus alternatives such as find_tools, find_services, or company_dimensions. It doesn't explain the context or exclusions. The reader is left to infer based on the resource name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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TDQS
C2.7/5.0
Disambiguation4/5
Most tools have distinct purposes with clear descriptions, reducing ambiguity. However, some overlap exists between search tools like 'find_posts' and 'search_api_evangelist', though they target different scopes (stories vs. unified search). Overall, an agent can reasonably differentiate them.
Naming Consistency3/5
The majority of tools follow a verb_noun pattern (e.g., find_areas, get_post), but several use noun_noun or inconsistent prefixes (e.g., api_coverage, company_gaps, insights_adoption). This inconsistency can confuse pattern recognition, though the pattern is still readable.
Tool Count2/5
With 56 tools, the server is overloaded for a typical MCP context. While the domain is broad, the sheer number risks agent confusion and selection errors. Calibration suggests 25+ tools are excessive, and this server far exceeds that threshold.
Completeness4/5
The tool set covers a wide range of API governance, search, analysis, and generation tasks. There are no obvious dead ends for navigating the API Evangelist network, though some areas (e.g., direct API creation) are intentionally out of scope. Minor consolidation could improve efficiency.