rtcStats

list_sessions

Lists 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.

Input Schema

TableJSON Schema

Name	Required	Description
`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"]).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`data`	Yes
`total`	Yes

TDQS

A4.3/5.0

Behavior4/5

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.

Conciseness4/5

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.

Completeness4/5

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.

Parameters4/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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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TDQS

A4.4/5.0

Disambiguation5/5

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.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (get_quota, get_session, list_sessions), making the API predictable and easy to navigate.

Tool Count4/5

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.

Completeness4/5

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.

Resources

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