This server is a read-only MCP interface to AGmind's registry of measured local-LLM benchmark claims, letting you search, retrieve, and enumerate benchmark evidence with citations.
Search claims: keyword search across headline, metric, system, model, runtime, scope, and id; case-insensitive, all whitespace-separated terms must match; returns summaries with value, unit, evidence level, permalink, and citation.
Get full claim: fetch a single claim by id with the complete statement, measured value, workload scope, limitations, evidence level, raw run ids/links, derivation SQL, permalink, and citation string.
List measured configurations: view distinct system × model × runtime combinations that have published claims, including claim counts and example ids.
Safe, read-only operation: no writes, telemetry, or accounts; the server only fetches and caches the registry from agmind.ai (or a mirror via AGMIND_CLAIMS_URL) and performs substring matching.
MCP integration: can be added to MCP clients like Claude Code or Claude Desktop via npx from the pinned GitHub release, and communicates over stdio.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@agmind-mcp search claims for TTFT on Strix Halo with 32k context"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP server for measured local-LLM benchmarks. It exposes the AGmind Systems Lab claim registry, currently 40 published claims measured on AMD Strix Halo hardware (Ryzen AI Max+ 395, Radeon 8060S, 128 GB unified memory) running llama.cpp on Vulkan and ROCm backends, as three read-only Model Context Protocol tools. Two NVIDIA DGX Spark (GB10) nodes are on the same lab bench; their claims enter the registry as runs are published. The lab has separately published vLLM work on DGX Spark; registry claims for it follow the same pipeline.
Every claim is a specific measured number: time to first token, inter-token latency, task success rate, answerless-response rate, long-context needle success, endurance drift. Each carries the exact hardware, runtime build, model revision and quantization, a frozen workload scope, stated limitations, an evidence level, links to the raw run records, and a ready-made citation string. Values are re-derived from raw runs on every CI build of the registry, so the numbers a model quotes through this server match the published evidence.
Requires Node.js 18 or newer. No install step is needed; npx fetches the server from GitHub. Pin the release tag (#v1.0.1) so what runs on your machine is a fixed, reviewable commit rather than whatever main holds at the moment you start it. The package is not published on npm: only the github: form below is ours, and a package named agmind-mcp on the npm registry would not be.
Claude Code
claude mcp add agmind -- npx -y github:botAGI/agmind-mcp#v1.0.1Claude Desktop (claude_desktop_config.json) and other MCP clients that take the standard config shape:
{
"mcpServers": {
"agmind": {
"command": "npx",
"args": ["-y", "github:botAGI/agmind-mcp#v1.0.1"]
}
}
}Releases and their commits: https://github.com/botAGI/agmind-mcp/releases.
From a local clone:
npm install
node server.mjs # speaks MCP over stdio
npm test # spawns the server and drives a real MCP sessionRelated MCP server: Local AI MCP
All three tools are read-only. Results are JSON in a text content block, and every claim in every result carries its cite string and permalink so agents can attribute what they quote.
search_claimsKeyword search over headline, metric, system, model, runtime, scope, and id. Case-insensitive; every whitespace-separated term must match.
search_claims({ "query": "ttft 32k" })Returns {id, headline, value, unit, evidence_level, permalink, cite} per match. Useful queries: decode, answerless, ttft cache, rocm, task-success, endurance.
get_claimOne claim in full by id: the complete answer paragraph, measured value and unit, workload scope, aggregation, limitations, evidence level, raw run ids with GitHub links, the derivation SQL, permalink, and citation string.
get_claim({ "id": "strix.qwen36.docsession.c1.ttft-q2-32k-cache" })An unknown id returns an error listing the closest matching ids.
list_measuredThe distinct system × model × runtime combinations that have published claims, with claim counts and example ids. Call this first to see what has actually been measured.
list_measured({})Server code: Apache-2.0.
Claim data: CC BY 4.0, attribution AGmind Systems Lab (agmind.ai). Each tool result includes a per-claim cite string ready to paste; reuse of the numbers should keep the claim permalink.
Registry source: https://agmind.ai/claims.json. Raw run records and derivation SQL: botAGI/agmind-lab. Benchmark harness and corpora: botAGI/agmind-bench.
Methodology, evidence levels, and errata: agmind.ai/methodology, agmind.ai/errata.
Read-only. The server never writes anything anywhere.
No telemetry, no analytics, no accounts. The only network call is fetching the registry from agmind.ai.
The registry is fetched at startup and cached in memory for one hour; a failed refetch falls back to the cached copy. The fetch times out after fifteen seconds and the response is shape-checked (a claims array of records with string ids) before anything is served to a model. Set AGMIND_CLAIMS_URL to point at a mirror of the registry if needed.
Tool inputs are used only for case-insensitive substring matching over the cached registry; the server builds no regular expressions from them and runs nothing.
Fetch a single claim from the AGmind registry by id: full statement, measured value, unit, scope, limitations, evidence level, raw run ids and links, permalink, and the ready-made citation string. Ids look like "strix.qwen36.interactive2.c1.ttfa-nothink".
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Claim id, e.g. "strix.qwen36.longctx.c1.ttft-32k-en" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the description does not need to emphasize safety. It adds value by disclosing the full set of returned fields, including raw run ids, links, permalink, and citation string, which goes beyond the schema. No contradiction is present.
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 concise and front-loaded with the core action ('Fetch a single claim'), followed by a list of contents and an id example. Every sentence adds value, and the format is ideal for quick scanning.
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 single-id lookup tool with one parameter, complete schema coverage, and a readOnly annotation, the description fully covers what the agent needs to know: what it returns, what the id looks like, and the tool's non-mutating nature. No output schema exists, but the description lists the return fields explicitly.
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%, and the description reinforces the id format with an example ('strix.qwen36.interactive2.c1.ttfa-nothink') and mentions the id pattern. This adds practical guidance beyond the schema's basic type description, though the schema already documents the parameter adequately.
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 fetches a single claim by id and enumerates the exact fields returned, distinguishing it from siblings like list_measured and search_claims. The verb 'Fetch' and resource 'single claim from the AGmind registry' are specific, and the inclusion of an example id format adds clarity.
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 implies use when a specific claim id is known and full detail is needed, but it does not explicitly state when not to use it or mention alternatives. Sibling tools exist (list_measured, search_claims), so more explicit guidance could help, but the context is clear enough for a targeted lookup tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List the distinct system × model × runtime combinations that have published, measured claims in the AGmind registry, with claim counts and example claim ids (with permalinks). Start here to see what hardware and models the lab has qualified.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, so the description doesn't need to state it's a read operation. The description adds useful context about the output: includes claim counts and example claim ids with permalinks, which is beyond the annotations. However, it doesn't disclose any other behavioral traits like pagination or result ordering, which could be relevant for a list operation. The description adds some value but not extensive behavioral context beyond the schema and 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 concise and front-loaded. It states the core function in the first sentence, then adds a helpful guidance sentence about starting here. Every word earns its place; there is no fluff or repetition of schema/annotation information.
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 tool has no parameters and no output schema, the description does a good job explaining what the tool returns (combinations, counts, example IDs) and gives a clear use case. It could potentially mention the format of permalinks or how to interpret claim counts, but for a simple list tool with good guidance, it is complete enough. The sibling tools (get_claim, search_claims) provide further context for follow-up actions.
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?
This tool has zero parameters, so the schema provides no parameter information. The description doesn't need to explain parameters, but it does clarify what the output represents (distinct combinations, claim counts, example IDs). With 0 parameters, baseline is 4 per the rubric, and the description adds sufficient context about the tool's purpose and output.
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 lists distinct system × model × runtime combinations with measured claims, including claim counts and example claim IDs with permalinks. It distinguishes from siblings by mentioning 'measured claims' and 'published' in the AGmind registry, which sets it apart from get_claim (retrieving a single claim) and search_claims (searching claims).
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 clear context: 'Start here to see what hardware and models the lab has qualified.' This explicitly guides the agent to use this tool as an entry point for exploring measured configurations. However, it does not explicitly state when not to use it or mention alternatives like search_claims, so it misses the when-not/alternatives aspect for a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Keyword search over the AGmind claim registry of measured local-LLM benchmarks (hardware, runtime, model, metric, workload scope). Case-insensitive; every whitespace-separated term must match. Returns claim summaries with value, unit, evidence level, permalink, and a ready-made citation string. Use get_claim for the full record including limitations and raw run ids.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Keywords, e.g. "decode", "ttft 32k", "qwen vulkan", "answerless" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only and closed-world behavior. The description adds genuinely useful behavioral details beyond that: case-insensitive matching, the requirement that every whitespace-separated term matches, and the specific summary fields returned.
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?
Three tightly written sentences cover the resource, matching behavior, return contents, and the main alternative. Every sentence earns its place with no wasted words or repetition.
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 simple one-parameter search tool with no output schema, the description is thorough: it states the registry scope, search semantics, return fields, and the natural follow-up tool for deeper detail. Nothing essential is missing.
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% and the schema already documents the single query parameter with examples. The description adds extra meaning by explaining the matching rule ('every whitespace-separated term must match') and the scope of keywords across hardware, runtime, model, metric, and workload.
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 states a specific verb and resource: 'Keyword search over the AGmind claim registry of measured local-LLM benchmarks.' It also clearly distinguishes from get_claim by directing users to that tool for full records, and the keyword-search framing differentiates it from list_measured.
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 gives clear context for when to use the tool and explicitly points to get_claim as the alternative for full records. However, it does not explicitly address when list_measured would be preferred over this keyword search.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0get_claimlist_measuredsearch_claimsEach tool serves a clearly distinct purpose: get_claim retrieves a single record by ID, list_measured provides an overview of measured combinations, and search_claims performs keyword queries. There is no overlap in functionality or potential for misselection.
All tool names follow a consistent snake_case verb_noun pattern (get_claim, list_measured, search_claims), which is predictable and easy to remember. No mixed conventions or ambiguous verbs.
With 3 tools, the set is compact yet sufficient for the server's purpose of querying a claim registry. Each tool earns its place, covering retrieval, overview, and search without unnecessary bloat.
For a read-only registry, the surface is complete: users can search, list overall combinations, and fetch full details by ID. There are no missing lifecycle operations (create/update/delete) that would apply to this domain, so no dead ends remain.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
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