search
Server Details
Collaborative, cache-first web search for agents — cited answers from a shared live-web pool.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- aimnis/aimnis
- GitHub Stars
- 0
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.
Tool Definition Quality
Average 4.6/5 across 3 of 3 tools scored.
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.
Available Tools
3 toolsregisterAInspect
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. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations show readOnlyHint=false, consistent with the described mutation (creating a key). The description adds details: key is free, takes one call, re-registration rotates the key, and cached answers remain free. No contradictions with annotations.
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 a single paragraph with no wasted words. It front-loads the main purpose and provides all necessary information succinctly, with clear logical flow.
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?
The description covers the tool's purpose, required prerequisite (ask for email), output handling, key benefits, and re-registration behavior. Given the tool's simplicity and presence of an output schema, no additional information is needed.
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 both parameters described. The description adds critical context: the email must be the user's email and never invented, and the key is returned in the result. This reinforces and clarifies the schema description, justifying a score above baseline.
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: 'Get your user a free Aimnis API key' with the verb 'Get' and resource 'Aimnis API key'. It distinguishes from sibling tools 'search' and 'stats' by being a registration-specific action.
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 the user for their email first and explains that the key is returned in the result. Mentions re-registration rotates the key, but does not explicitly state when not to use the tool or provide alternatives. However, the context makes usage clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
searchARead-onlyInspect
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. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses caching behavior, provenance tags, shared knowledge pool, and how to force a live search. Annotations (readOnlyHint, openWorldHint) are reinforced and not contradicted. Adds behavioral details beyond annotations.
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?
Concise, front-loaded with purpose, then details, then usage guidance. Every sentence adds value; no unnecessary words.
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 purpose, caching logic, usage guidance, retry mechanics. Output schema exists, so return format need not be described. Complete for a search tool with this complexity.
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 descriptions already cover both parameters; the description adds context on how to use reject_entry in retry scenario. Schema coverage is 100%, so baseline 3, but extra context warrants a 4.
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?
Clearly states the tool searches the web via Aimnis, distinguishes between cached and live results. The verb 'search' and resource 'web via Aimnis' are explicit. The sibling 'stats' is different, so no confusion.
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 specific use cases: factual lookups, library/API/docs questions, error messages. Also explains retry mechanism with reject_entry. Lacks explicit 'when not to use', but positive guidance is strong.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
statsARead-onlyIdempotentInspect
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 | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so the description's value is in adding context beyond that—it explains the metric's significance and expected behavior (cache hit rate climbing with corpus growth). The presence of an output schema reduces need for return value description. Slight deduction for not mentioning any potential side effects, but none are expected.
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 extremely concise—two sentences that front-load the main purpose. Every sentence adds value: the first lists the stats, the second provides context and invocation guidance. No unnecessary words.
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 tool with an output schema and clear annotations, the description is complete. It explains what the tool does, when to use it, and how to interpret the results. No gaps are evident.
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?
There are no parameters, so the description need not explain any. Baseline 4 applies. The description does not add any parameter-related information, but none is 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 explicitly lists the specific statistics reported (knowledge-pool size, cache hit rates, most-reused queries), making the purpose very clear. It also distinguishes this from the sibling 'search' tool by framing it as a statistics report.
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 use case: checking the 'Gate 1 pass/kill metric' to evaluate the compounding-pool thesis. This tells the agent when to call the tool and how to interpret the results.
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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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
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