Collaborative, cache-first web search for agents — cited answers from a shared live-web pool.
Get your user a free Aimnis API key (no credit card, takes one call).
Ask your user for their email address first. The key comes back in this tool
result — relay it to the user so they can save it and add it to your MCP
connection ('Authorization: Bearer aim_...'). A key raises the daily
live-search limits; cached answers are always free, with or without a key.
Re-registering with the same email rotates (replaces) that email's key.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | Your USER'S email address — ask them for it first, never invent or guess one. The key is returned in this result and a copy is emailed to this address. | ||
| use_case | No | Optional one-line note on what the key will be used for. |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that the tool is a write operation (creating/rotating keys), the key is returned in the result and emailed, and re-registering replaces the key. Annotations already indicate non-readOnly, so description adds valuable context about user permission and key handling without contradiction.
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?
Description is well-structured and front-loaded with the key purpose. It includes necessary details but could be slightly more concise; however, every sentence adds value.
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 simple tool with full schema coverage, annotations, and an output schema, the description is complete. It explains the result format, key usage, and limits, leaving no ambiguity for an AI agent.
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% with detailed descriptions for both parameters. The tool description reiterates the need for the user's own email and the optional use_case, but adds no new semantic meaning beyond what the schema provides.
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?
Description clearly states the tool obtains a free Aimnis API key for the user, specifying no credit card needed and one call. The verb 'get' and resource 'Aimnis API key' are specific, and it distinguishes from sibling tools 'search' and 'stats' which serve different purposes.
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 instructs to ask user for their email first, warns against guessing, and explains what to do with the resulting key. Also notes re-registration behavior, providing clear when-to-use and when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the web via Aimnis.
Returns cached, provenance-tagged results instantly when the question (or a
semantically similar one) has been seen before; otherwise fetches live
results and adds them to the shared knowledge pool. Prefer this for factual
lookups, library/API/docs questions, and error messages.
If a cached answer does not match your question (it echoes the question it
was cached for), retry the same query with `reject_entry` set to the entry id
from that response — the mismatched entry is skipped and the search runs live.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The natural-language question, error message, or library/API/docs lookup to search for. | |
| reject_entry | No | Entry id from a previous response whose cached answer did not match your question; set it to skip that entry and force a live search on retry. |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes cached vs live results, provenance-tagging, shared knowledge pool, and retry mechanism. Annotations (readOnlyHint, openWorldHint) are consistent and supplemented.
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?
Efficient two paragraphs with front-loaded purpose. Could be slightly tighter, but no 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?
Covers all relevant behavior, use cases, and retry logic. With annotations and output schema, no gaps for agent decision-making.
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 has 100% coverage, but description adds practical context for reject_entry (skip cached entry on mismatch), enhancing beyond schema.
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 it searches the web via Aimnis and explains caching behavior. It distinguishes from siblings (register, stats) which are unrelated.
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?
Provides explicit guidance: prefer for factual lookups, library/API/docs questions, error messages. Also details when to retry with reject_entry if cached answer mismatches.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Report Aimnis flywheel statistics: knowledge-pool (cache) size, cache hit rate (all-time and recent), and the most-reused queries.
This is the Gate 1 pass/kill metric — cache hit rate should climb as the
corpus grows. Call it to see whether the compounding-pool thesis is holding.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint. The description adds specific details on what statistics are reported (cache size, hit rates, queries), going beyond the annotations without contradiction.
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 sentences, each essential. First sentence states what the tool reports; second gives the business context and motivation. No verbose or redundant content.
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 zero-parameter, read-only tool with an output schema, the description explains the returned metrics and the strategic reason to call it. This provides complete context for proper usage.
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?
Input schema has zero parameters, and schema coverage is 100%. The description adds no parameter info, but baseline for 0 params is 4. No explanation needed.
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 it reports Aimnis flywheel statistics (cache size, hit rate, most-reused queries). The verb 'report' and specific resource are explicit, distinguishing from sibling tools 'register' and 'search'.
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 a clear usage context: 'Call it to see whether the compounding-pool thesis is holding' and identifies it as a Gate 1 pass/kill metric. It lacks explicit 'when not to use' or alternative tool comparisons, but the context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user or an account that owns the GitHub organization, then choose Claim with GitHub.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
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
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
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!
Agent-native search engine with live web research optimized for AI agents.
LLM-ready web search + instant answers + URL-to-clean-text fetch for agents and RAG.
Web search for AI agents — one tool across 6 engines, routed to the cheapest + cached.
Search the agentic web. 4,100+ sites, 11 tools incl. check_url + verify_mcp for probe-before-use.
Add one secure layer between your agents and this server.
Each tool has a distinct purpose: register for API key, search for queries, stats for cache metrics. No overlap.
All tool names are single, lowercase words, but they mix verb (register, search) and noun (stats). Consistent style but not a strict pattern.
Three tools is minimal but reasonable for a search server covering key management, search, and stats. Could benefit from one or two more.
Covers core operations: registration, search, and metrics. Missing cache management or detailed entry inspection, but sufficient for basic use.
