Find ~5 thinkers whose intellectual fingerprint matches a passage of text.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4.5/5 across 1 of 1 tools scored.
There is only one tool, so there is no possibility of confusion with other tools. Its purpose is clearly defined and distinct.
The single tool name follows a clear verb_noun pattern ('find_thinkers_like'), which is descriptive and consistent with standard conventions.
The server has a single tool, which feels thin for a server purpose that could potentially include additional related capabilities (e.g., retrieving thinker profiles or comparing multiple passages). However, the tool is self-contained and clearly scoped, making the count borderline.
The tool fully covers its stated purpose of finding similar thinkers based on a text passage, including rich output with archetype, summary, themes, and profile link. There are no obvious missing operations for this niche domain.
find_thinkers_likeFind thinkers like thisAInspectGiven a passage of text (essay, note, message, snippet, transcript), returns ~5 humans whose intellectual fingerprint matches it — recurring themes, mental models, archetypal stance, blind spots. Use when the principal asks for sparring partners, intellectual peers, "who else is wrestling with this," "who thinks like X," or "find me writers similar to this passage." Each result returns a name, three-word archetype, one-line summary, dominant themes, and a profile URL the principal can visit. The match runs over Voyage 3.5-lite text embeddings reranked by a proprietary 12-dimensional cognitive-style vector — so results align by how a mind reasons, not just topical overlap.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
| Name | Required | Description |
|---|---|---|
| rerank | Yes | |
| matches | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It explains the matching algorithm (Voyage 3.5-lite embeddings reranked by a proprietary 12-dimensional cognitive-style vector) and the output structure, which adds meaningful depth. However, it stops short of describing edge cases, failure modes, or permission requirements.
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 compact and well-structured. The opening sentence delivers the core function, followed by usage guidance, output details, and the underlying algorithm. Every sentence contributes value without redundancy.
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 presence of an output schema (which the description also explains in words) and the simple one-parameter input, the description fully covers what an agent needs to select and invoke the tool. It states the input format, expected output fields, and the reasoning behind matches.
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 sole parameter 'text' has no description in the schema, so the tool description compensates by specifying the acceptable forms: 'essay, note, message, snippet, transcript.' This adds practical meaning beyond the plain string type, though it does not mention the length constraints or other details.
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 tool's purpose with a specific verb ('returns') and resource ('~5 humans whose intellectual fingerprint matches it'). It explains the matching criteria and lists the output fields, leaving no ambiguity about what the tool does.
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 provides explicit use cases: 'Use when the principal asks for sparring partners, intellectual peers, "who else is wrestling with this," "who thinks like X," or "find me writers similar to this passage."' It does not mention when not to use it or name alternatives, but given there are no sibling tools, this is acceptable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!