Playbook

List workspace members

list_members

Read-only

List the people in a Playbook workspace, or find one by name. Pass query to search by name or email — the match is fuzzy, so a partial or misspelled name works ('quackenbsh' finds 'Zephyrine Quackenbush'). Returns each member's token, which is the value the uploader filters on ai_search, search_assets, and list_assets require. Call this FIRST whenever a request names a person ('what did Max upload', 'files from Sarah') — a person's name typed into a search query does NOT match the person who uploaded an asset. A name can match several members; each comes back with its own token.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_members"
  }
}

Input Schema

Name	Required	Description
`page`	No	Page number (1-indexed)
`query`	No	Find members by name or email. Fuzzy match, tolerates typos and partial names. Omit to list everyone. Ignored when shorter than 2 characters.
`per_page`	No	Items per page (1-100, default 20)
`organization`	No	Workspace slug, as returned by list_organizations. Omit it when the account has a single workspace — it is then resolved from your token. Pass it only when the account has several, or when you mean a workspace other than the obvious one.

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedNov 3, 2026
- removedInput schema / additionalProperties
```
Removed value: -false
```
- addedInput schema / properties / page / maximum
```
Added value: +9007199254740991
```

Changed3 schema fields changedOct 5, 2026

addedInput schema / additionalProperties
```
Added value: +false
```

removedInput schema / properties / context

Removed value: -{
-  "description": "Explain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): \"Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization.\"",
-  "type": "string"
-}

removedInput schema / required
```
Removed value: -[
-  "context"
-]
```

First observedOct 2, 2026

TDQS

A4.6/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already establish readOnlyHint=true, destructiveHint=false, and a closed world, so the safety profile needs no restatement. The description adds behavior annotations cannot express: fuzzy matching tolerates misspellings ("quackenbsh" finds "Zephyrine Quackenbush") and results carry per-member tokens that must be fed into downstream filters. It says nothing about pagination or result ordering, though the schema's page/per_page params make that a minor omission.

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 workflow instruction (call this first) is front-loaded after a one-clause purpose statement, and each sentence contributes a distinct fact: search mechanism, return value, routing rule, ambiguity caution. Slightly long, and "List the people ... or find one by name" is partially restated by the following sentence, but there is no padding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the burden of explaining the return value, and it does so precisely through the `token` field and its downstream use. It covers the undocumented behavioral edge cases (fuzzy match, multiple matches, the name-in-search-query pitfall) that matter for correct invocation.

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 baseline is 3, but the description adds meaning the schema does not: it explains why a fuzzy match can return multiple members and that each has its own token, which affects how the agent consumes the `query` parameter's results. The typo example reinforces what the schema states more tersely, so the gain is real but modest.

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?

States a specific verb and resource ("List the people in a Playbook workspace") plus a secondary retrieval mode ("find one by name"). It explicitly contrasts with ai_search, search_assets, and list_assets by noting those tools' uploader filters require the token this tool returns, so an agent can distinguish it from the search siblings without reading schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit routing rule: "Call this FIRST whenever a request names a person," with two concrete request examples and the failure mode of the alternative ("a person's name typed into a search query does NOT match the person who uploaded an asset"). It also flags the ambiguity case where one name yields several members, so the agent knows to disambiguate rather than assume a single hit.

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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