Done Bear MCP

Search Tasks

search

Read-only

Search tasks across the workspace. Ranked lexical search over title and description: exact and prefix matches rank first, then word-boundary, substring, and fuzzy matches, with recency and status factored in. Multi-word queries match in any order.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No	Max results
`query`	Yes	Search query (matches title and description)
`state`	No	Filter by task state	all
`cursor`	No	Opaque pagination cursor from a previous call's `nextCursor`. Pass it with the same filters to fetch the next page.
`context`	Yes	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."
`timezone`	No	IANA timezone used to resolve "today" for views and scheduling (e.g. Australia/Melbourne). Defaults to UTC, which misclassifies Today/Upcoming for users in other timezones, so always pass the user's timezone when known.
`workspace`	No	Workspace id, slug, or name

Output Schema

TableJSON Schema

Name	Required	Description
`count`	Yes
`query`	Yes
`tasks`	Yes
`total`	Yes
`hasMore`	Yes	True when more results matched than were returned
`nextCursor`	Yes

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already mark this as read-only and non-destructive. The description goes further by exposing the ranking algorithm (exact/prefix, word-boundary, substring, fuzzy), the influence of recency and status, and multi-word matching order. This is valuable behavioral context beyond what annotations provide.

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?

Three sentences, each earning its place: the first states the core purpose, the second explains ranking mechanics, the third handles multi-word queries. No repetition of schema content and no filler.

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?

Given that an output schema exists and all parameters are described in the schema, the description can focus on the search semantics, which it does well. It does not explain pagination or cursor usage, but those are already covered by the schema's cursor parameter description. The coverage is sufficient for a tool of this complexity.

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 detail about the query parameter: exact/prefix/substring/fuzzy matching tiers and multi-word order independence. It also clarifies that status affects ranking, which enriches the state parameter's role beyond its simple enum definition.

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 and resource: 'Search tasks across the workspace.' It further distinguishes this from listing/showing tools by detailing a ranked lexical search over title and description, which clearly separates it from sibling tools like task_list or task_show.

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

Usage Guidelines3/5

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

The description clearly implies usage for text-based search with ranking behavior, but it never explicitly states when to choose this over task_list or how it differs from other task-finding tools. There are no exclusions or alternative routing cues, leaving the decision largely to inference.

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

Most tools are cleanly separated by resource and action, and each family (checklist, comment, label, project, task) follows the same shape. The main blur is between search and task_list's optional search filter, and convenience tools like checklist_done overlap with checklist_edit.

Naming Consistency4/5

The dominant convention is snake_case noun_verb (task_add, checklist_list, comment_remove), with consistent verb families across resources. A few outliers like get_context, search, and guide break the pattern, but overall the naming is predictable and readable.

Tool Count2/5

At 35 tools, this is a heavy surface for a task-management server and exceeds the 25-tool threshold. Several tools are convenience variants (task_done vs task_bulk_done, checklist_done vs checklist_edit) that could be consolidated or represented as parameters.

Completeness4/5

The core task lifecycle is well covered: create, show, edit, complete, reopen, archive, list, assign, and bulk operations, with full checklist and comment CRUD. Notable gaps are label deletion/renaming, project deletion, and search being limited to tasks, but these are minor for the apparent product scope.

Resources

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