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talonic-mcp

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by talonicdev
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TypeScript
Remote

Ask a question over the workspace

talonic_ask

Ask a natural-language question about your documents and receive a cited, verified answer in markdown. It plans over extracted data, runs read-only SQL, and grounds claims in source spans.

Instructions

Ask a natural-language question over the workspace's documents and get a cited, verified answer (markdown). The Talonic agent plans over the structured field plane, runs read-only SQL over extracted cells, reads document text, and grounds every load-bearing claim in a source span. Consumes credits.

USE WHEN: the user asks an open question about their documents ('which vendors invoiced us twice in May?'), wants a summary across documents, or the answer needs reasoning over several fields. NOT FOR: reading a known field's values (talonic_field_values, free) or filtering documents by a known value (talonic_filter, free); locating which field holds a concept (talonic_find_data). ARGS: question; optional scope { document_ids[], schema_id, pipeline_id, data_product_id, document_type, source_id, tags[], ingested_after, ingested_before } (ANDed), conversation_id (continue a thread), output_format { instruction, template }, wait_seconds (0–55, default 45). RETURNS: { ask_id, status ('completed'|'processing'|'error'), conversation_id, answer (markdown), citations[] { quote, document_id, kind, filename, app_url }, verification { verdict, checks_total, checks_unsupported, correction }, usage { tokens, credits_charged }, tool_calls, artifacts[], waited_ms }. If status is still 'processing' after the wait, call talonic_get_answer with the ask_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scopeNoRestrict the question to a slice of the workspace; present fields are ANDed.
questionYesThe question, in the user's words.
wait_secondsNoSeconds to wait for the answer before returning 'processing' (default 45, max 55).
output_formatNoShape the answer (form only, never grounding).
conversation_idNoContinue this conversation; the agent sees prior turns.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.81

TDQS

A4.7/5.0
Behavior5/5

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

The description discloses internal mechanics: planning over the structured field plane, running read-only SQL over extracted cells, reading document text, and grounding claims in source spans. It also reveals the cost ('Consumes credits') and the asynchronous behavior with instructions to call talonic_get_answer if status is still 'processing'. This goes well beyond the annotation hints.

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 organized into clear sections: purpose, when to use, when not to use, args, and returns. The purpose is front-loaded, and every sentence carries routing, invocation, or behavior information. Despite being long, the length is justified by the tool's complexity and there is no filler.

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 tool's complexity and the absence of an output schema, the description is self-sufficient: it covers parameters, scoping, return shape, cost, verification, and the async continuation path. The explicit instruction to call talonic_get_answer when status is 'processing' closes the loop for correct use.

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

Parameters3/5

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

Schema description coverage is 100%, and the schema already documents all parameters including ANDed scope semantics, wait_seconds default, and output_format behavior. The description's ARGS section provides a useful at-a-glance summary but little new semantic information beyond the schema, so the baseline score of 3 applies.

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 opens with a specific verb and resource ('Ask a natural-language question over the workspace's documents') and names the deliverable: a cited, verified markdown answer. It clearly distinguishes this tool from sibling tools by describing it as for open, multi-field reasoning questions. There is no ambiguity about what the tool does.

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 has explicit USE WHEN and NOT FOR sections that name the sibling alternatives (talonic_field_values, talonic_filter, talonic_find_data) and even note that they are free. It states concrete conditions for choosing this tool over those alternatives, so an agent can route correctly without guessing.

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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