TENEKS MCP

Describe this TENEKS workspace

describe_workspace

Read-onlyIdempotent

Report whose recordings this connection can read, how many there are, the date range they cover, and the exact call-type and rep names that filters accept.

Call this first. The other tools filter on labels that must match the workspace's own vocabulary, and guessing one is the most common reason a query comes back empty when data exists.

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": "describe_workspace"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description
`reps`	No	The same reps with stable ids. Pass rep_id to list_conversations when a name is ambiguous.
`role`	Yes	Their role in the workspace.
`rep_names`	No	Rep names that work as a rep_name filter. A name shared by two people is omitted here and appears only in reps, because rep_name cannot choose between them -- use rep_id for those.
`user_name`	No	The connected user.
`call_types`	No	Exact call-type labels this workspace uses, for filters.
`environment_name`	No	Workspace name.
`latest_call_date`	No	ISO date of the newest visible recording.
`visibility_scope`	Yes	own_calls, team or environment -- whose recordings this connection can read.
`conversation_count`	Yes	Finished recordings visible to this connection.
`earliest_call_date`	No	ISO date of the oldest visible recording.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 4, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint=false, so safety is covered. The description adds genuine behavioral context beyond them: that the output is the authoritative vocabulary downstream filters must match, and that skipping it causes silent empty results. It stops short of describing output structure, but an output schema exists.

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?

Two short paragraphs, no filler, with the report contents front-loaded and the prerequisite instruction following. The sequencing cue is arguably the more urgent information and sits second, which is a minor structural inefficiency but not wasteful.

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 zero parameters, an output schema present, and rich annotations, the description supplies exactly the missing piece: why this call must precede the others. Nothing an agent needs to decide to invoke it is absent.

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?

The tool takes zero parameters, so there is no parameter semantics to explain and the baseline is 4. The description correctly spends no words on arguments and instead explains what the parameterless call yields.

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 ("Report") tied to concrete resources: whose recordings are readable, the count, the date range, and the exact call-type and rep names. It is clearly distinguishable from siblings like get_transcript or list_conversations, which return data rather than workspace metadata.

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?

It gives an explicit sequencing instruction ("Call this first") plus the reason: the other tools filter on labels that must match the workspace's own vocabulary. It even names the failure mode being avoided — empty results despite existing data — which tells an agent exactly when this tool is mandatory.

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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