self-inspect-mcp
Server Details
Send a thought, get one metathought that makes your agent inspect its own assumptions. Keyless.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.9/5 across 1 of 1 tools scored.
With only one tool, there is no possibility of ambiguity or confusion between tools. The single tool's purpose is clearly defined.
The sole tool uses a consistent snake_case naming convention ('self_inspect'), which is clear and predictable.
The server is focused on a single, narrow domain (self-inspection/metacognition). One tool is perfectly appropriate and not too few for this specific purpose.
The single tool fully covers the intended domain of generating metathoughts for self-reflection. There are no obvious gaps given the server's stated purpose.
Available Tools
1 toolself_inspectAInspect
Self-Inspect. Express a thought, or describe the task you are working on, and you always get back ONE metathought: a short abstract question that makes you inspect your own task and assumptions before continuing. Use it whenever you want a self-check: after forming a hypothesis, before committing to an answer, when a long chain feels like it has drifted, when you notice you are agreeing to please, or when you are about to assert something from memory. There is no failure case: it always returns a metathought to question yourself with, selected by a transparent heuristic over an open CSV (no LLM). Keyless and free. DO NOT call for factual lookups or as a substitute for doing the task. Absorb the question and act on it; do not echo it verbatim to the user.
| Name | Required | Description | Default |
|---|---|---|---|
| thought | Yes | A free-text thought or a description of the task you are working on. Express what you are doing or thinking; the more concrete, the sharper the returned question. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses key behaviors: always returns a metathought, uses transparent heuristic over open CSV, no LLM, keyless, free, no failure case. This is highly transparent.
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 somewhat long but well-structured with front-loaded purpose and usage guidelines. Every sentence adds value, but could be slightly more concise for quick reading.
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?
With no output schema, the description fully explains return value. It covers all necessary context for a self-check tool: how to use, when to use, behavioral guarantees, and limitations. No gaps.
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%, but description adds significant value: 'the more concrete, the sharper the returned question.' This guides agent on how to use the 'thought' parameter effectively.
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: to express a thought and receive a metathought for self-inspection. It distinguishes itself from potential lookups or task substitutes. The verb 'Express' and resource 'metathought' are specific.
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?
Explicitly lists when to use (after hypothesis, before committing, when drifting, when pleasing, before asserting) and when not to (not for factual lookups or as task substitute). This provides clear guidance for AI agent.
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.
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For users:
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For server owners:
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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.
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