talonic-mcp

Official

by talonicdev

Overview Schema Related Servers Score Discussions(1)

TypeScript

Remote

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.73
Disambiguation5/5
Each tool has a clearly distinct purpose: filtering, schema management, document retrieval, search, text conversion, extraction, credit/usage queries. Detailed descriptions and 'NOT FOR' notes prevent ambiguity.
Naming Consistency5/5
All tools follow a consistent 'talonic_verb_noun' pattern (e.g., list_schemas, get_document, get_balance). The only slight deviation is 'to_markdown', but it's still clear and fits the pattern.
Tool Count5/5
With 11 tools, the set is well-scoped for the server's purpose: document upload, search, filter, extract, convert, and schema/credit management. No unnecessary tools or obvious missing ones.
Completeness4/5
The tool surface covers the core workflow (upload, search, filter, extract, convert, manage schemas, credits). Minor gaps exist (e.g., no delete or update for documents/schemas), but these do not hamper typical use.
Average 4.8/5 across 9 of 11 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 81 commits in the last 12 weeks
- Last stable release on July 22, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds value by detailing the return structure (period_days, total_credits, by_function) and the days parameter clamping. This extra context goes 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?
The description is concise, well-structured with clear sections (lead sentence, USE WHEN, NOT FOR, ARGS, RETURNS). Every sentence is purposeful 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 tool's simplicity (one optional parameter, read-only operation with annotations and output schema implied by the return description), the description fully covers when, why, and what to expect. It is complete for effective 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 coverage is 100%, so baseline 3. The description repeats the parameter's default and clamping, adding slight value over the schema description. However, no additional meaning beyond what the schema already conveys.
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 'Read the workspace's per-function credit consumption' using a specific verb and resource. It distinguishes from siblings by explicitly stating it is for seeing where credits went, not for balance or pricing.
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 'NOT FOR' sections, directing the agent when to use this tool and providing alternatives (talonic_get_balance, talonic_get_pricing). This makes the usage context crystal clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate non-read-only and non-destructive behavior. The description adds valuable context like cost ('cheap per call, free tier'), confidence-based review thresholds, and mentions of returned metadata. No contradictions with annotations.
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 lengthy but well-structured with clear sections (USE WHEN, NOT FOR, BY NAME, ARGS, COST, RETURNS). It front-loads the core purpose. Every sentence adds value, though some repetition could be trimmed. It earns high marks for organization.
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 11 parameters (0 required) with complex interdependencies, the description covers all document source options, schema definition methods, auto_schema, instructions, and optional output fields. It also describes the return structure with confidence scores and hints for handling low confidence. Output schema enriches this, but the description provides essential context for a complete picture.
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 meaning by explaining mutual exclusivity rules (schema vs schema_id vs auto_schema), recommending usage for each document source, and clarifying optional parameters like instructions and include_markdown. This is a meaningful addition beyond the schema.
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's purpose as converting unstructured documents into structured JSON. It specifies the verb ('turn into'), resource ('document'), and explicitly distinguishes it from sibling tools like talonic_to_markdown and talonic_search/talonic_filter by listing use cases and exclusions.
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 when-to-use patterns ('USE WHEN: extract data from this document...'), exclusions ('NOT FOR: full plain text...'), and a prerequisite step ('BY NAME: if the user names a file, call talonic_search first'). This is excellent guidance for an AI agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true. The description adds valuable behavioral details: the status lifecycle, terminal failure statuses, and instructions to stop polling and report failures. No contradiction.
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 concise and well-structured with clear sections (USE WHEN, NOT FOR, BY NAME, ARGS, RETURNS). Every sentence adds value, and the formatting aids readability.
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 simplicity (1 parameter, output schema exists), the description covers purpose, usage, behavior, parameter origin, return values, and status lifecycle. It also integrates with siblings for complete workflow guidance.
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 coverage is 100% and describes the document_id parameter well. The description adds context about getting the ID from previous responses, but does not provide new semantic meaning beyond what the schema offers. Baseline 3 is appropriate.
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 'Fetch a single document's metadata and processing status from the workspace.' It uses a specific verb and resource, and distinguishes itself from sibling tools (talonic_to_markdown, talonic_extract) by stating what it is NOT for.
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?
Explicit 'USE WHEN' and 'NOT FOR' sections provide clear context. It specifies when to poll after upload, when to use alternatives, and even instructs to call talonic_search first if user names a file.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds value by stating it returns data[] of schema summaries, that full field definitions are omitted, and that there are no arguments. This provides behavioral detail beyond the annotations.
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 concise with three clear sections: purpose, usage guidance, and returns/args. No unnecessary information. Every sentence serves a purpose.
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?
For a simple listing tool with no parameters and minimal complexity, the description covers all necessary information: what it returns, how to use it, and where to find additional details (resource). Output schema exists but the description adequately summarizes the return.
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?
There are no parameters, and the description confirms 'ARGS: none.' The schema coverage is effectively 100%. With 0 parameters, the baseline is 4, and the description meets that.
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 starts with a specific verb ('List') and resource ('saved schemas'), and details the output fields (id, short_id, name, description, version, field_count). It distinguishes from sibling tools by noting that full field definitions are omitted and directing users to the talonic://schemas resource for those.
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?
Explicitly states when to use ('what schemas do I have', find a reusable schema before extracting) and when not to (one-off extraction with inline schema, directing to talonic_extract). This provides clear alternatives and context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate destructiveHint=false, which aligns with saving non-destructive. Description adds that it saves to workspace and returns id/short_id. Could mention if same name overwrites, but overall sufficient.
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?
Extremely concise: one sentence purpose, then usage, then args, then returns. No wasted words.
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?
All necessary context provided given schema coverage and output description. Parameter count, required fields, and return value all addressed.
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?
Input schema has 100% coverage. Description adds value by explaining definition accepts JSON Schema or flat map, and that return includes id and short_id for later use.
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?
Clear verb 'Save' with specific resource 'a reusable schema to the workspace'. Distinguishes from sibling talonic_extract by stating it's for reuse across documents, not one-off extractions.
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?
Explicitly states 'USE WHEN' and 'NOT FOR' with direct alternative (talonic_extract). Provides clear context for when to choose this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations give readOnlyHint and destructiveHint. Description adds: conditions are AND-ed, operator-specific behaviors (is_empty takes no value), numeric guard (check dataType first), text field name resolution with warnings, and instruction to surface warnings. No contradiction.
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 clear sections (USE WHEN, NOT FOR, ARGS, TEXT FILTERS, NUMERIC GUARD, RETURNS) and front-loaded. It's somewhat long but each sentence adds value, appropriate for the complexity.
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 (6 params, nested objects, many operators) and existence of output schema, the description covers all critical aspects: purpose, usage, parameter details, edge cases, warnings, return structure. Nothing missing.
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%, but description adds deep semantics: explains field/field_id exclusivity, operator list, value/value_to for between, data types, and text filter resolution. This goes well beyond schema descriptions.
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 'Find documents by their extracted field VALUES using composable conditions' and gives an example. It distinguishes from siblings talonic_search and talonic_get_document in the 'NOT FOR' section.
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?
Explicitly says 'USE WHEN: value-based criteria on extracted fields' and 'NOT FOR: free-text / concept search (use talonic_search); a single document by id (use talonic_get_document).' Also provides guidance on numeric guard and warning handling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark it read-only. The description adds value by detailing returned fields and noting that projected runway is -1 when no recent usage. No contradiction with annotations.
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?
Description is concise: a single introductory sentence followed by structured USE WHEN, NOT FOR, ARGS, and RETURNS sections. Every sentence is informative and earns its place.
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 simplicity (read-only, no parameters, documented output schema), the description covers all necessary context: what is returned, when to use, and what not to use for. No gaps.
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?
Input schema has 0 parameters (100% coverage). Description explicitly states 'ARGS: none.' Baseline for 0 parameters is 4, and the tool meets that standard without needing further parameter documentation.
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 it reads the workspace's Talonic credit balance, EUR value, tier, 30-day burn, and projected runway. The verb 'Read' and specific resource details make purpose unambiguous. It naturally distinguishes from credit-related queries that would go to talonic_extract.
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?
Explicit USE WHEN and NOT FOR sections provide clear guidance: use when user asks about credits/budget or before a large batch; not for per-call cost (pointing to talonic_extract). This effectively differentiates from sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds value by stating it is public and does not spend credits, which is consistent with the annotations. No contradictions.
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 concise and well-structured with clear headings (USE WHEN, NOT FOR, ARGS, RETURNS). Every sentence is informative and earns its place. No wasted words.
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 zero parameters and an output schema (though not shown), the description fully covers what the agent needs: purpose, usage, and return format. The annotations provide safety context, making it complete.
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?
With zero parameters and 100% schema coverage, baseline is 4. The description mentions 'ARGS: none' and explains the return structure (currency, credits_per_eur, etc.), which adds meaning beyond the empty schema.
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 'Read' and identifies the resource 'Talonic's machine-readable credit pricing catalog'. It clearly distinguishes from sibling tools like talonic_get_balance and talonic_get_usage by stating its purpose is for estimating costs.
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?
Explicitly provides use cases ('USE WHEN: estimating cost'), non-use cases ('NOT FOR: balance/usage'), and names alternative tools. This leaves no ambiguity about when to invoke this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses full workflow: returns upload_url, document_id, expires_at; after user uploads, poll talonic_get_document until status 'completed', then call talonic_extract; handles failure statuses. Annotations (readOnlyHint=false) are not contradicted and are supplemented by this detailed behavioral context.
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 sections: main description, USE WHEN, NOT FOR, ARGS, RETURNS. It is front-loaded with key information, and every sentence adds value. No unnecessary words or repetitions.
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 (one parameter, no enums, output schema exists), the description completely covers the input, output, and subsequent steps. It provides a workflow for polling and error handling, which is essential for an agent to use the tool correctly. Sibling tools are listed for 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?
The schema already describes filename with minLength and a brief description. The description adds that filename should include extension and is used to pre-allocate and infer MIME type, providing extra meaning beyond the schema. With 100% schema coverage, baseline is 3; added value justifies 4.
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's purpose: 'Get a browser upload link the user opens to add a file to their workspace' and specifies it returns both the link and a pre-allocated document_id. It uses specific verbs and resources, and the context distinguishes this from sibling tools like talonic_extract.
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?
Explicit 'USE WHEN' and 'NOT FOR' sections provide clear guidance on when to use this tool versus alternatives (e.g., 'hosted/sandboxed clients' or 'files too large'), and when not to (e.g., document already in workspace or file on public URL). The description also directs the agent to talonic_extract for public URLs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds critical behavioral details: matching is literal keyword, sentences/plurals return empty, and fields must have filterable:true to work with talonic_filter. No contradiction.
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?
Efficient structure: first sentence states purpose, then details matching behavior, then usage guidelines, then parameter description, then returns listing. Every sentence serves a purpose. No wasted words.
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 output schema exists, the description is complete enough. It covers purpose, behavior (literal matching, empty results), usage constraints, return types with actionable ids, and integrates with sibling tools. No gaps.
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 baseline is 3. Description adds value by clarifying that query must be a short singular keyword or exact filename, and gives examples like 'invoice' vs 'invoices'. Also states limit default is 5. This utility boosts the score above baseline.
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 specific verbs and resources: 'Find documents, fields, schemas, or sources'. It clearly distinguishes from siblings by noting that this tool is for keyword search across all types, while talonic_filter is for structured filters.
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?
Explicit conditions: 'USE WHEN: the user names or describes a document without an id... NOT FOR: structured field-value filters (use talonic_filter).' Also provides retry advice for empty results, which is actionable context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint=false, destructiveHint=false, openWorldHint=true), description adds that file inputs ingest and consume credits while document_id does not, and different input methods have different costs.
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?
Well-structured with sections, front-loaded with main purpose. Every sentence adds value. No redundancy.
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 output schema exists, description covers return values (document_id and markdown). Addresses complex input choices and usage scenarios. Complete for tool 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% with good descriptions. Description adds hierarchy: prefer document_id, then file_url, then file_data+filename for small local files. Provides selection guidance beyond schema.
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?
Description clearly states it gets OCR-converted markdown text of a document. Uses verb 'Get' and specifies resource. Distinguishes from sibling talonic_extract for structured fields.
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?
Explicit sections: USE WHEN for full text/summarize/translate, NOT FOR structured fields, BY NAME instructs to first call talonic_search. Provides clear decision rules.
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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