Query analyzed WebRTC sessions: observations, deductions, experience scores, and AI summaries.
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Average 4.5/5 across 3 of 3 tools scored.
Each tool serves a clearly distinct purpose: get_quota handles account information, get_session retrieves a single session by ID, and list_sessions provides filtered search. No overlap in functionality or ambiguity in selection.
All tool names follow a consistent verb_noun pattern (get_quota, get_session, list_sessions), making the API predictable and easy to navigate.
With just 3 tools, the set is minimal but coherent for a read-only RTC stats server. The count feels slightly thin but each tool covers a distinct need without redundancy.
The core workflows (check quota, list sessions, fetch session details) are covered. A notable gap is the lack of a tool to access the observation catalog mentioned in get_session, which would be useful for interpreting observation types.
get_quotaAInspectReturns the application token account: accountName, accountPlan (e.g. free, developer, trial_developer, enterprise), allowedCredits and remainingCredits (monthly plus purchased pool). Requires Authorization: Bearer on the MCP HTTP request.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| accountName | Yes | |
| accountPlan | Yes | |
| allowedCredits | Yes | |
| remainingCredits | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the transparency burden. It discloses the auth requirement and the exact composition of returned data (monthly plus purchased pool). It doesn't discuss error behavior or read-only status, but for a simple quota tool this is largely sufficient.
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 sentence that front-loads the primary purpose, then details the return fields and auth requirement without unnecessary 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 no-parameter, read-only quota tool with an output schema present, the description covers the essential context: return field details and authentication. Nothing important seems 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?
The tool has zero parameters, so baseline for parameter semantics is 4. The description adds no parameter information because none exist; schema coverage is effectively complete.
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 returns the application token account with specific fields (accountName, accountPlan, allowedCredits, remainingCredits). It uses a specific verb ('Returns') and resource ('application token account'), distinguishing it from sibling session 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?
The description implies usage when quota/credit details are needed and clearly states the authentication requirement (Authorization: Bearer <application JWT>). It does not explicitly mention when not to use or name alternatives, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_sessionAInspectReturns full session payload for one stored session (summary, observations, scores, etc.). Arguments: rtcstatsId (UUID). In data.observations[], each observation has type, severity, category, tags, firstSeenAt and a source object whose properties depend on the category: source.pid (peer connection id), source.sid (stream/datachannel report id), source.cpid (candidate-pair id), source.did (device entry id), source.rid (device row index), source.timestamp (device entry time), source.ssrcId (number, stream SSRC), source.labelId (device name). The top-level label is DEPRECATED and removed in the next schema version (overloaded legacy id; read source.ssrcId / source.sid / source.labelId instead). The full catalog of observation types (with possible severities and tags) is available from GET /v1.0/observations. Requires Authorization: Bearer on the MCP HTTP request.
| Name | Required | Description | Default |
|---|---|---|---|
| rtcstatsId | Yes |
| Name | Required | Description |
|---|---|---|
| data | Yes | |
| embedUrl | No | |
| rtcstatsId | Yes | |
| rtcstatsUrl | Yes | |
| processorVersion | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the required Authorization header, the deprecation of the top-level label, and the conditional structure of source properties based on category. It also references an external catalog for observation types, adding meaningful behavioral context.
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 information-dense and well-structured: it starts with purpose, then parameter, then details of observations, deprecation, catalog reference, and auth. Every sentence adds value, and the length is appropriate for a tool with a complex response payload.
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's complexity (nested observations with category-dependent source objects) and the lack of annotations, the description is thorough. It covers auth, deprecation, data structure, and external references, providing sufficient context for an agent to use the tool correctly.
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 only parameter rtcstatsId is described as a UUID, which adds semantic meaning beyond the schema's generic 'string' type. However, it does not explain how to obtain or validate the ID, and with 0% schema coverage, the description only partially compensates.
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 returns a full session payload for one stored session, using a specific verb ('Returns') and resource ('full session payload'). It distinguishes from siblings by emphasizing 'one stored session' versus list_sessions (listing) and get_quota (quota).
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: use this tool when you have a specific rtcstatsId and need full session details. It does not explicitly mention alternatives or exclusions, but the context is sufficient to infer when to use it over list_sessions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_sessionsAInspectLists stored sessions for the application token account: rtcstatsId, createdAt, sessionStart, sessionEnd, title, rtcstatsUrl, embedUrl (Enterprise only), and abstract (denormalized session summary: browser/os, observation types+tags, severity counts, score band). Optional filters: name (title substring, any of), observationTypes (exact type names, any of), observationTags (any of), os, browser, browserVersion, userId, conferenceId, sessionId, hasCritical/hasHigh/hasMedium, hasLowScore/hasMediumScore/hasHighScore. Different filters are AND-combined; multiple values within one filter are OR-combined. Example queries: sessions about "zoom" on Safari + macOS, sessions with network observation tags, sessions having critical observations, sessions with a low experience score. Requires Authorization: Bearer on the MCP HTTP request.
| Name | Required | Description | Default |
|---|---|---|---|
| os | No | Filter sessions by operating system (e.g. "macOS", "Windows", "Linux", "Android", "iPhone", "iPad"). | |
| name | No | Filter sessions whose title contains ANY of these substrings (case-insensitive). E.g. ["zoom"] for all sessions about Zoom. | |
| userId | No | Filter sessions by the end-user identifier carried in the rtcstats JWT (summary metadata user). | |
| browser | No | Filter sessions by browser (e.g. "Chrome", "Safari", "Firefox", "Edge"). | |
| hasHigh | No | When true, only sessions with at least one high-severity observation. | |
| hasMedium | No | When true, only sessions with at least one medium-severity observation. | |
| sessionId | No | Filter sessions by the session identifier carried in the rtcstats JWT (summary metadata session). | |
| hasCritical | No | When true, only sessions with at least one critical-severity observation. | |
| hasLowScore | No | When true, only sessions whose experience score is low (< 60). | |
| conferenceId | No | Filter sessions by the conference identifier carried in the rtcstats JWT (summary metadata conference). | |
| hasHighScore | No | When true, only sessions whose experience score is high (>= 80). | |
| browserVersion | No | Filter sessions by major browser version (e.g. "142"). | |
| hasMediumScore | No | When true, only sessions whose experience score is medium (60 to 79). | |
| observationTags | No | Filter sessions whose observations carry ANY of these tags. Valid tags: connectivity, security, audio, video, datachannel, outbound, inbound, peripheral, behavior, network, configuration, cpu, bug. | |
| observationTypes | No | Filter sessions that contain ANY of these observation type strings (exact type name, e.g. ["turnNoConfig"]). |
| Name | Required | Description |
|---|---|---|
| data | Yes | |
| total | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does well: it discloses the returned fields, the denormalized abstract summary, filter combination semantics, the Enterprise-only caveat for embedUrl, and the required Authorization header. It does not mention pagination, max results, or ordering, but the core behavior is transparent.
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 long but dense and well-organized. It front-loads the returned fields and then moves to filters and examples. Every section serves a purpose, though the text is longer than strictly necessary. Given the complexity, this is justified.
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 15 parameters, no annotations, and a complex filter model, the description covers the essential dimensions: fields returned, filter semantics, examples, auth requirements, and an enterprise field caveat. The output schema handles return-value details. Minor gaps like pagination limits or default sort order prevent a perfect score.
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%, so the baseline is 3. The description adds meaningful value beyond the schema by explaining that filters are AND-combined while multiple values within a filter are OR-combined, and it provides example query patterns. It does not repeat per-parameter details, but the combination logic is the most important semantic information.
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 uses a specific verb ('Lists stored sessions') and identifies the resource (sessions for the application token account). It enumerates the returned fields and contrasts with the sibling get_session by emphasizing the plural list nature and broad filtering capabilities. This makes the tool's purpose unmistakable.
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 usage context: optional filters, AND/OR combination rules, and concrete example queries. It does not explicitly name get_session or get_quota as alternatives, but the listing-vs-single-session distinction is apparent from the name and field list. This is strong but not an explicit when-to-use vs alternatives statement.
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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