Skip to main content
Glama

get_overview

START HERE. What the API Evangelist network offers: sixteen years of API research as searchable stories, 77 topic areas, governance building blocks (guidance, rules, policies, standards, strategies), papers, conversations, and the consulting services Kin Lane offers teams. Returns counts, entry points, and how to engage.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A3.8/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the burden. It states that the tool returns counts, entry points, and engagement guidance, which is meaningful behavioral information. It does not explicitly state that the operation is read-only or describe response structure, but the 'get' verb and 'returns' phrasing make side effects unlikely.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense sentence with the key instruction front-loaded. The list of content areas is somewhat lengthy but helps an agent understand the scope of the overview.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter tool with no output schema, the description tells the agent what it will get back (counts, entry points, engagement) and why to call it first. It is complete enough to invoke correctly, though a note on read-only behavior would round it out.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes no parameters, so the schema already fully documents the input surface. The description adds useful context about what the returned counts and entry points cover, but no parameter-level detail is needed.

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 opens with 'START HERE' and clearly identifies the tool as an entry point to the API Evangelist network, listing what it offers and what it returns. This distinguishes it from more specific siblings like get_stats or get_area, though it doesn't name a sibling explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

'START HERE' is an explicit signal that this tool should be the first call an agent makes, providing clear usage context. It does not name alternatives or exclusion conditions, so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources