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
The four tools have largely distinct roles: overview discovery, semantic search, single-page fetch, and feedback submission. The only mild overlap is between searchLangfuseDocs and getLangfuseDocsPage, but the descriptions clearly explain when to use each.
All names use camelCase with clear verb_noun style: getLangfuseDocsPage, getLangfuseOverview, searchLangfuseDocs, submitFeedback. Minor inconsistency is that some names include the Langfuse prefix and others do not, but the pattern remains predictable.
Four tools is well-scoped for a documentation access server. Each tool serves a distinct purpose: discover docs, search docs, fetch a specific page, and submit feedback.
The surface covers the core documentation workflow: index discovery, semantic search, exact page retrieval, and user feedback. There are no obvious missing operations for this domain.
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 Their code samples reflect the SDK/API at release time and may be outdated, so for implementation follow the current docs and the API/SDK reference instead.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?
Annotations already declare readOnlyHint=true, so safety is covered; the description adds genuine value by disclosing the return format (exact Markdown, possibly with front matter) and warning that changelog code samples may be outdated. It does not mention failure behavior for invalid paths (404/fallback), which keeps it from a 5.
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?
Front-loaded with purpose, then usage, then the alternative; every sentence is functional. The changelog caveat runs long across two sentences, but it carries non-obvious guidance rather than 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 read tool with no output schema, the description covers return format, accepted inputs, selection against the sibling, and the changelog staleness pitfall. Nothing an agent needs to call it correctly 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 accepted path/URL forms and that anchors and queries are ignored. The description's parenthetical example and "accepts a full https://langfuse.com URL" restate rather than extend the schema, so the baseline 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?
Opens with a specific verb and resource: "Fetch the raw Markdown for a single Langfuse docs page," and immediately constrains scope to one page. By naming searchLangfuseDocs as the multi-page alternative, it is clearly distinguishable from its 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?
Gives explicit when-to-use ("a specific page content ... or code samples") and when-not-to-use/alternative ("Prefer searchLangfuseDocs for broader questions where there is not one specific page"). It also carves out changelog pages with a specific condition for their use (confirm existence or ship date, not implementation reference).
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?
readOnlyHint=true already tells the agent this is a safe read; the description adds that it performs a network download from a specific URL, that it returns plain text, and that repeated calls should be avoided in a session (implicit caching/cost guidance). It does not quantify size or state failure behavior, but that is minor for a zero-parameter read.
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?
Four short sentences, each doing distinct work: fetch target, trigger condition, return format, and anti-pattern warning. The trigger condition is front-loaded after the identity statement, and nothing is repeated.
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 zero parameters and no output schema, the description supplies exactly the missing pieces: what is fetched, that the return value is plain text of llms.txt, and how it relates to the sibling retrieval tools. No gap remains for an agent deciding whether to call it.
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 tool takes no parameters, so the baseline is 4. Nothing in the description is needed to explain inputs, and it correctly does not invent any.
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?
States a specific verb (downloads/gets) and a concrete resource (the llms.txt index at a named URL), and explicitly frames it as an index rather than a doc page. The sibling tools getLangfuseDocsPage and searchLangfuseDocs are named, so an agent can distinguish this entry-point tool from the retrieval tools without opening any schema.
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?
Gives an explicit when ('at the start of a session'), a purpose ('to discover key docs endpoints or to seed follow-up calls'), alternatives ('searchLangfuseDocs or getLangfuseDocsPage'), and a when-not ('Avoid repeated calls within the same session'). This is the full when/when-not/alternatives pattern.
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. Prefer this before guessing. If a specific page is needed call getLangfuseDocsPage first. The raw provider response is included in _meta.
| 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?
Annotations already cover the safety profile (readOnlyHint=true), and the description adds real value beyond them: it discloses that the return is a synthesized answer rather than raw results, and that the raw provider response is surfaced in _meta. It stops short of noting accuracy/recency caveats or result-count behavior, so not a 5.
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?
Front-loaded with the core capability, then routing, then return shape — a sensible ordering. Slightly chatty in the middle, where "Prefer this before guessing" and "If a specific page is needed..." partially restate the same routing idea, but nothing is wasted enough to be a 3.
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 read tool with no output schema, the description covers everything an agent needs: what it does, when to pick it over the sibling, what comes back, and where the raw response lives. No meaningful gap remains.
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 query parameter description already explains natural-language phrasing, context to include (SDK version, self-hosted vs cloud), and the ~600 character limit. The tool description adds no parameter meaning beyond that, so the baseline 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?
States a specific verb and resource ("Semantic search (RAG) over the Langfuse documentation") and immediately scopes it against the sibling: broader questions vs. a single page served by getLangfuseDocsPage. An agent can choose between the two without opening either schema.
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?
Explicit routing rules: use this for broader questions that a single page cannot answer, and call getLangfuseDocsPage first when a specific page is needed. "Prefer this before guessing" adds an explicit fallback ordering, which is exactly the when-to-use signal agents need.
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
Add one secure layer between your agents and this server.