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task_find_similar

Find top-K most similar existing tasks to a candidate name using lexical similarity. Use before creating a task to detect duplicates and decide whether to create, link, or merge.

Instructions

Lexical nearest-neighbour search for de-duplicating tasks. Pass a candidate name (and optional note) and receive the top-K most-similar existing tasks ranked by a deterministic [0, 1] lexical-signal score (Jaccard token-overlap + prefix bonus + exact-name boost). Title-dominant: a perfect title match outranks a perfect note match. Use BEFORE task_create when you suspect a duplicate; the agent inspects the candidates and decides whether to create new, link to existing, or merge. Excludes completed and dropped tasks by default; opt-in via includeCompleted: true. Optional scope { projectId } or { tagId } narrows the candidate set. Returns { candidates: [{ taskId, name, score, project, tags }] } sorted by score descending — project is { id, name } | null and tags is [{ id, name }, ...]. Names are paired alongside ids via a single getProjectsMany + single getTagsMany batch (no N+1) so the agent can describe each candidate without a follow-up read. An empty result is { candidates: [] }, not an error. Do NOT use this tool for general full-text search — call task_search for that. Prefer this helper when the question is 'is this task already in the system?'. No model calls; no side effects. Read-only. Example: task_find_similar({ name: "Call dentist" }) Example: task_find_similar({ name: "Write report", scope: { projectId: "prj123" }, topK: 5 })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesThe candidate task name to compare against existing tasks.
noteNoOptional note text. When both the candidate and an existing task have a note, note overlap contributes to the score as a tiebreaker.
limitNoTop-K candidates to return. Default 5, max 50.
scopeNoNarrow the candidate set to one project or one tag. Mutually exclusive — supply at most one. Omit to search all open tasks.
includeCompletedNoWhen true, include completed and dropped tasks. Default false (open tasks only).
Behavior5/5

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

With no annotations provided, the description fully discloses all behavioral traits: it is read-only, has no side effects, no model calls, uses a deterministic lexical scoring algorithm, excludes completed tasks by default, supports optional scope narrowing, returns sorted candidates, and describes the output format including empty result handling. It even notes performance optimizations (batch queries).

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?

The description is well-structured: it opens with the core purpose and algorithm, then usage guidelines, parameter details, output format, and a performance note. Every sentence adds value without redundancy. Despite being moderately long, it is efficiently organized and front-loaded with key information.

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 5 parameters including a nested scope object, a custom scoring algorithm, and no output schema, the description provides comprehensive coverage. It explains input semantics, the algorithm, output format (including field descriptions), default behaviors, and error cases. The agent can fully understand how to use the tool without additional context.

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%, but the description adds significant semantic value beyond the schema: it explains the scoring algorithm, the role of 'note' as a tiebreaker, the mutual exclusivity and purpose of 'scope', the default for 'includeCompleted', and provides examples. While the schema already documents each parameter, the description enriches understanding of how they interact.

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 the tool is a 'Lexical nearest-neighbour search for de-duplicating tasks.' It specifies the verb (search/find), the resource (tasks), and distinguishes from siblings like task_search and task_find_by_name by explicitly stating the deduplication use case. The purpose is specific and unambiguous.

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 explicit usage guidance: 'Use BEFORE task_create when you suspect a duplicate' and 'Do NOT use this tool for general full-text search — call task_search for that.' It also mentions alternatives like task_find_by_name, giving clear when-to-use and when-not-to-use conditions.

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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