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talonic-mcp

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Filter Talonic Documents

talonic_filter

Filter documents by extracted field values using conditions like equals, contains, or date comparisons. Combine multiple filters for precise results.

Instructions

Filter the user's Talonic documents by extracted field values using composable conditions. Conditions accept either a canonical field name (e.g. 'vendor.name', 'policy.0_coverage_type') or a field UUID. The Talonic API resolves names to ids server-side.

USE WHEN:

  • The user wants documents matching specific structured criteria, like 'invoices over 1000 EUR' or 'contracts expiring before 2026-12-31' or 'COIs from Acme'.

  • The query is value-based on extracted fields, not a free-text concept search.

  • You need to retrieve a sortable, paginated list filtered by field conditions.

DO NOT USE WHEN:

  • The user wants conceptual / free-text search across content (use talonic_search).

  • The user is looking for a single document by id (use talonic_get_document).

  • The user wants extracted data from a new document (use talonic_extract).

OPERATORS:

  • eq, neq: equality / inequality

  • gt, gte, lt, lte: numeric or date comparisons

  • between: requires both value and value_to

  • contains: substring match on string fields

  • is_empty: presence check (no value needed)

  • is_not_empty: presence check (no value needed). Note: currently underreports; use eq / gt / contains etc. against a known value when possible.

TIPS:

  • To discover available field names, call talonic_search first with a related query. fields[].canonicalName from the response is what to pass as field here.

  • Both field (name) and field_id (UUID) reach the API as fieldId. Either is fine.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
conditionsYesOne or more filter conditions, AND-ed together.
searchNoOptional free-text search applied alongside the filters.
sortNoOptional sort by a field.
pageNoPage number for pagination.
limitNoResults per page. Default 50 server-side.
source_connection_idNoOptionally scope to a specific source connection.

Tool Definition Quality

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 fully covers behavioral traits: it explains condition composition, operator semantics (including a caveat about is_not_empty underreporting), field resolution, pagination, and sorting. It also provides tips for discovering available fields, giving agents a comprehensive understanding of tool behavior.

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 with clear sections (USE WHEN, DO NOT USE WHEN, OPERATORS, TIPS) and each sentence serves a purpose. It is concise yet thorough, with no wasted words.

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?

The description is highly complete given the tool's complexity, covering usage, operators, tips, and integration with sibling tools. However, it lacks details about the output format (e.g., pagination metadata, field structure in results), which would be beneficial since no output schema is provided.

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?

Despite 100% schema coverage, the description adds significant meaning beyond the schema: it clarifies that conditions are AND-ed, details operator behaviors and limitations, explains the mutual exclusivity of `field` and `field_id`, and provides usage tips like calling talonic_search first to discover field names.

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 filters Talonic documents by extracted field values using composable conditions. It differentiates from siblings by explicitly mentioning when to use and not to use, such as for conceptual search (talonic_search) or single document retrieval (talonic_get_document).

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 includes explicit 'USE WHEN' and 'DO NOT USE WHEN' sections with concrete examples like 'invoices over 1000 EUR' and references to alternative tools, providing unambiguous guidance on when to apply this tool.

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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