Official Langfuse documentation for building LLM evaluation workflows: datasets, experiments, LLM-as-a-judge evaluators, custom scores, human annotation, and prompt/model comparison. Search documentation and retrieve current code examples, including tracing and SDK integrations. Remote Streamable HTTP server; no account, API key, or local installation required. Provides documentation guidance; accessing project data and running evaluations require separate setup.
Scored across 4 tools
Each tool serves a clearly distinct purpose: overview for discovery, semantic search for broad questions, page fetch for specific content, and feedback for user submissions. There is no overlap between search and page fetch because the descriptions explicitly direct when to use each.
All tool names follow a consistent camelCase verb-noun pattern (getLangfuseOverview, getLangfuseDocsPage, searchLangfuseDocs, submitFeedback). The verbs vary according to action but the naming style is uniform and predictable.
Four tools is well-scoped for a documentation server: one for indexing/overview, one for search, one for page retrieval, and one for feedback. Each tool earns its place without redundancy or bloat.
The core documentation workflow is covered: discover via overview, search semantically, and fetch specific pages. A minor gap is the lack of a dedicated tool to list all available pages or sections, though llms.txt supplied by the overview tool partially addresses this.
Fetch the raw Markdown for a single Langfuse docs page. Accepts a docs path (e.g., /docs/observability/overview) or a full https://langfuse.com URL. Returns the exact Markdown (may include front matter). Use when you need a specific page content (Integration, Features, API, etc.) or code samples. Prefer searchLangfuseDocs for broader questions where there is not one specific page about it. Changelog pages (/changelog/...) are historical release notes: use them only to confirm that a feature exists or when it shipped, not as an implementation reference. Their code samples reflect the SDK/API at release time and may be outdated, so for implementation follow the current docs and the API/SDK reference instead.
| Name | Required | Description | Default |
|---|---|---|---|
| pathOrUrl | Yes | Docs path starting with "/" (e.g., /docs/observability/overview) or a full URL on https://langfuse.com. Do not include anchors (#...) or queries (?foo=bar) — they will be ignored. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool returns exact Markdown possibly including front matter, that anchors and query strings are ignored, and warns that changelog code samples may be outdated. This gives the agent a clear behavioral model of what the tool does and its limitations.
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 well-structured and front-loaded with the core purpose, then input formats, then usage guidance, and finally the changelog caveat. Every sentence adds value, and while it is relatively long, it is dense with actionable information and free of filler.
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-parameter tool with no output schema, the description fully covers what the agent needs: accepted inputs, what is returned, when to use it versus alternatives, and caveats about changelog pages. Nothing essential is missing for correct invocation.
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?
Although the schema already documents the pathOrUrl parameter with 100% coverage, the description adds extra meaning by explicitly stating anchors (#...) and queries (?foo=bar) will be ignored, and clarifies accepted formats (path or full URL). This goes beyond the schema's basic type description.
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 raw Markdown for a single Langfuse docs page, specifies accepted input formats (path or full URL), and distinguishes itself from searchLangfuseDocs by noting when to prefer that sibling for broader questions. This precisely differentiates it from siblings.
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 prefer searchLangfuseDocs for broader questions, and provides detailed guidance on changelog pages: they are historical release notes, to be used only to confirm existence or release timing, not as implementation reference. This is clear when-to-use and when-not-to-use guidance with alternatives named.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get a high-level, machine-readable index by downloading https://langfuse.com/llms.txt. Use this at the start of a session when needed to discover key docs endpoints or to seed follow-up calls to searchLangfuseDocs or getLangfuseDocsPage. Returns the plain text contents of llms.txt. Avoid repeated calls within the same session.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations, so description carries the burden. It discloses the return (plain text contents) and advises caching-like behavior ('avoid repeated calls'), but doesn't mention error handling or network specifics. Given it's a simple read-only fetch, this is adequate.
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 concise sentences with zero filler. Action is front-loaded, then usage, then return, then caution. 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?
Complete for a no-parameter read tool. It explains what it returns, when to use it, and how it relates to siblings. No output schema needed since return is plain text.
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?
Tool has zero parameters, so baseline 4. The description correctly avoids any param discussion; schema already covers everything.
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 action: 'Get a high-level, machine-readable index by downloading https://langfuse.com/llms.txt.' It names the specific resource and what it returns, and differentiates from siblings by naming them as follow-up tools.
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 states when to use: 'at the start of a session' and for 'discover key docs endpoints or to seed follow-up calls', names the alternative tools, and warns against repeated calls. Provides clear usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Semantic search (RAG) over the Langfuse documentation. Use this whenever the user asks a broader question that cannot be answered by a specific single page. Returns a concise answer synthesized from relevant docs. The raw provider response is included in _meta. Prefer this before guessing. If a specific page is needed call getLangfuseDocsPage first.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The user's question in natural language. Include helpful context like SDK/language (e.g., Python v3, JS v4), self-hosted vs cloud, and short error messages (trim long stack traces). Keep under ~600 characters. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It discloses that the tool performs RAG, returns a synthesized answer rather than raw docs, and includes the raw provider response in _meta. It does not mention limitations or failure modes, but it provides meaningful behavioral context beyond the basic action.
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 front-loaded, with each sentence earning its place: purpose, usage condition, return behavior, _meta note, and sibling alternative. 'Prefer this before guessing' is slightly vague but adds a useful behavioral nudge without unnecessary length.
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 one-parameter search tool with no output schema and no annotations, it covers the key facts an agent needs: what it searches, when to use it, what it returns, and which sibling to call instead. It doesn't discuss cost, latency, or RAG limitations, but these are minor gaps for this tool type.
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 input schema already covers the single query parameter at 100% with detailed guidance about natural language, helpful context, and character limit. The description itself adds no parameter-level semantics, so the baseline of 3 applies.
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 opens with a specific verb and resource: 'Semantic search (RAG) over the Langfuse documentation.' It clearly distinguishes itself from a specific-page lookup by stating it returns 'a concise answer synthesized from relevant docs,' which is a different outcome than fetching a single page.
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?
It explicitly says when to use it: 'Use this whenever the user asks a broader question that cannot be answered by a specific single page.' It also names the alternative: 'If a specific page is needed call getLangfuseDocsPage first,' giving clear routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Submit explicit user-approved feedback to the Langfuse team. Before calling, ask permission and show the exact payload. If the user wants a reply, ask them to include their email address in the feedback text; only use an address they explicitly provide and show it in the exact payload preview. Do not include secrets, credentials, customer/project data, trace payloads, or unrelated context; the only contact detail to include is an explicitly provided reply email.
| Name | Required | Description | Default |
|---|---|---|---|
| goal | No | ||
| target | Yes | The specific instance within targetType: the skill name, MCP tool name, CLI command, API endpoint path, or docs page path (e.g. 'queryMetrics', '/docs/mcp'). An identifier, not a sentence. | |
| feedback | Yes | ||
| targetType | Yes | Category of the thing the feedback is about. | |
| referenceUrl | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It discloses the consent requirement, the need to preview the exact payload, and privacy/data-hygiene constraints such as excluding credentials, trace payloads, and unrelated context. It does not describe response behavior, but the main behavioral risks around submission are addressed.
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 front-loaded with the purpose, followed by concise preconditions and guardrails. Every sentence adds actionable information, with no filler or 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?
The description covers purpose, permission, payload preview, and content restrictions. However, with no output schema or annotations, it does not state what happens after submission—whether a confirmation is returned, whether feedback is queued, or how the agent should interpret the result. This is a notable but not critical gap for a simple submission tool.
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 only 40%; goal, feedback, and referenceUrl are undocumented in the schema. The description does not define these parameters, though it adds content-level constraints such as putting the reply email in the feedback text and excluding secrets/credentials. This partially compensates for the schema gap but leaves the meaning of goal and referenceUrl to inference.
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 opens with a specific verb and object: 'Submit explicit user-approved feedback to the Langfuse team.' This clearly states the tool's function and distinguishes it from sibling doc-retrieval tools, which read documentation rather than send feedback.
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 procedural guidance: ask permission, show the exact payload, include an email only if the user explicitly provides one and wants a reply, and avoid secrets or sensitive data. It does not explicitly route away from sibling tools, but those siblings serve a different purpose, so no exclusion is necessary.
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.
getLangfuseDocsPagegetLangfuseOverviewsearchLangfuseDocssubmitFeedback
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