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list_knowledge

List knowledge files and folders for this company (names, slugs, sizes, folders). search matches file NAMES only — not body text. Read a body with read_knowledge by slug. Always-on files live in canon/ (injected into chat and skill gen within a size budget); everything else is on-demand via read_knowledge. Use when discovering what knowledge exists before reading a file.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
folderNoOptional folder to list contents of (e.g., "canon", "partners"). Omit to list the root level. Always-on docs live in canon/.
searchNoOptional name-only filter (filename/slug). Does not search file bodies — use read_knowledge by slug for content.
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and does so thoroughly. It discloses the name-only search limitation, the fact that bodies require a separate read call, and the always-on vs. on-demand behavior of canon/ files, including the size-budget context. This goes well beyond the minimal expectation for a read-only listing tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four tight sentences, each earning its place. The core purpose is front-loaded, followed by the search caveat, the read_knowledge routing, the canon/ behavior, and a direct usage instruction. No redundancy with the schema or filler content.

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?

For a 3-parameter list tool with no output schema and no annotations, the description is complete: it covers scope, output fields, search semantics, sibling routing, and the canon/always-on vs. on-demand distinction. Nothing essential for correct invocation is missing.

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 value by explaining returned value semantics ('names, slugs, sizes, folders') and the significance of the canon/ folder (injected into chat and skill gen, size-budgeted), which enriches the folder parameter beyond the schema. It also reinforces the search parameter's name-only constraint.

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 knowledge files and folders'), the scope ('for this company'), and the returned fields ('names, slugs, sizes, folders'). It also distinguishes itself from siblings by stating that search matches names only and that bodies are read via read_knowledge, so an agent can pick the correct tool without opening the schema.

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?

Explicitly instructs when to use the tool: 'Use when discovering what knowledge exists before reading a file.' It also names the alternative for reading content ('read_knowledge by slug') and clarifies the canon/on-demand distinction, giving the agent clear routing guidance.

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

A3.6/5.0
Disambiguation4/5

The tool set is heavily disambiguated by detailed routing descriptions, domain prefixes, and lifecycle verbs, so most tools have a clear intended purpose. However, at 297 tools there are still close pairs and overlapping decision surfaces (e.g., approval workflows, 'what should I work on' readers, multiple finance/ads readers) that require careful description reading to avoid misselection.

Naming Consistency4/5

Naming is predominantly consistent snake_case verb_noun with strong domain prefixes like shopify_, x_, posthog_, and list_/create_/update_ patterns. Minor inconsistencies exist, such as several collection-returning tools using get_ (get_team_members, get_icps, get_okrs) instead of list_, and some generate_ vs create_ vs draft_ verbs, but the pattern is still predictable overall.

Tool Count1/5

297 tools is an extreme outlier and far beyond a usable MCP tool surface. Even a large suite has no justification for this count in one server; the agent would struggle to select among hundreds of similarly descriptive tools, and the natural 3-15 tool range is exceeded by nearly 20x.

Completeness4/5

The individual domains represented — OKRs, CRM/leads, Shopify, content pipelines, ads, PostHog, team hiring, knowledge, finance, and session management — are covered remarkably well with full lifecycle patterns. Minor gaps exist, such as no full deal CRUD, no delete for several Google/Shopify artifacts, and some analytical surfaces being read-heavy, but most workflows can be completed without dead ends.

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