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query_dci_teams

Search DCI teams using a flexible query language with operators like eq, ilike, and contains. Filter by fields such as name, tags, and creation date.

Instructions

Lookup DCI teams with an advanced query language.

Listing all teams: To list all teams, use ilike(name,%) as the query.

The query language is based on this DSL:

eq(<field>,<value>) to lookup resources with a <field> having the value <value>.
IMPORTANT: Values must NOT be quoted. Use eq(name,DCI) not eq(name,'DCI').

You can use the comparison functions gt (greater than), ge (greater or equal),
lt (less than) or le (less or equal) using the same syntax as eq: <op>(<field>,<value>).

like(<field>,<value with percent>) and ilike(<field>,<value with percent>)
to lookup a field with a SQL glob with at least one % character. For example, to get the teams
with a specific name pattern, use like(name,%Name%).

contains(<field>,<value1>,...) and not_contains(<field>,<value1>,...)
to lookup elements in an array. This is useful mainly for tags.

and(<op1>(...),<op2>(...)), or(<op1>(...),<op2>(...)) and not(<op>) allow
to build nested boolean queries.

null(<field>) to lookup resources with a field having a NULL value.

Here are all the fields of a DCI team that can be used in the query:

- id: unique identifier

- name: name of the team

- created_at: The creation timestamp. Use `today` tool to compute relative dates.

- updated_at: The last update timestamp. Use `today` tool to compute relative dates.

- tags: list of tags associated with the team.

Listing all teams: To list all teams, use ilike(name,%) as the query.

Counting Teams: To get the total count of teams matching a query, set limit=1 and read the count field in the _meta section of the response.

Example for counting teams by name:

{
  "query": "eq(name,DCI)",
  "limit": 1,
  "offset": 0,
  "fields": []
}

This will return a response like:

{
  "teams": [],
  "_meta": {"count": 10},
  ...
}

The total count is 10 teams.

Returns: JSON string with list of teams and pagination info

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYessearch criteria (e.g., and(ilike(name,qa),contains(tags,ga))). To list all, use ilike(name,%)
sortNoSort criteria-created_at
limitNoMaximum number of results to return for pagination (default 20, max 200). Use limit=1 to get count from metadata.
offsetNoOffset for pagination
fieldsNoList of fields to return. Fields are the one listed in the query description and responses. Must be specified as a list of strings. If empty, no fields are returned.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations, the description fully carries the burden. It discloses the return format (JSON with list and pagination), the behavior of the query DSL (including the important note that values must not be quoted), how fields parameter works (empty returns no fields), and how to count. This is highly 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 well-structured with a summary, DSL explanation, field list, and examples. It is front-loaded. However, there is slight repetition (listing all teams mentioned twice) and it is fairly verbose, which prevents a perfect score. Nevertheless, every section adds value.

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?

Given the complexity of the tool (custom query DSL, pagination, counting) and the presence of an output schema (implied), the description covers everything needed: query syntax, list of queryable fields, counting method, pagination parameters, and a complete example. No gaps are present.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, baseline 3. The description adds significant value beyond the schema: it explains the query language, how limit is used for counting, the effect of fields being empty, sorting defaults, and provides examples. This greatly enhances parameter understanding.

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 clearly states 'Lookup DCI teams with an advanced query language.' It specifies the resource (DCI teams) and the action (lookup/query). Among sibling tools, it is the only one for querying teams, so it effectively distinguishes itself.

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?

The description provides extensive usage guidelines: how to list all teams using ilike(name,%), how to count teams via limit=1 and reading _meta.count, details on the query DSL with examples, and pagination. It covers when and how to use the tool comprehensively.

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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