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Summarize any document or URL

forcedream_summarize_document

Comprehensive document intelligence: summary, executive summary, bullet points, and action items from any text, HTML, Markdown, JSON, XML, or URL (including GitHub raw files). Never adds facts not in the source. SPENDS your balance -- requires authentication (OAuth).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceYesEither raw text/HTML/Markdown/JSON/XML content pasted directly, or a URL to fetch (including GitHub raw file URLs) -- both are handled automatically.
budget_penceNoOptional max spend in pence for this call.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
outputNo
statusYes
verifyNo
task_idNo
proof_idNo
balance_penceNo
charged_penceNo

TDQS

A3.7/5.0
Behavior4/5

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

Annotations declare openWorldHint=true, readOnlyHint=false, destructiveHint=false. The description adds valuable behavioral context: it 'never adds facts not in the source' (faithfulness guarantee) and openly warns it 'SPENDS your balance -- requires authentication (OAuth)' — critical financial and auth disclosure not captured in annotations. It does not describe output structure, but output_schema exists and covers that.

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 two sentences and front-loaded with the purpose, then quickly pivots to critical behavioral warnings (faithfulness, cost, auth). It's compact and every sentence earns its place. Minor verbosity in the format list, but overall efficient.

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?

For a 2-param tool with full schema coverage and an output schema present, the description covers purpose, input flexibility, faithfulness, cost, and auth. The main gap is not explaining return value granularity (e.g., what summary vs executive summary vs bullet points means), but the output schema partially covers structure. Given the tool's moderate complexity plus cost/auth transparency, this is well covered.

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 schema already documents both parameters. The description adds context that the source parameter accepts raw content OR a URL (both handled automatically), which reinforces the schema. budget_pence is not elaborated beyond the schema's 'max spend in pence' description, but the behavioral note about spending balance gives it context. This is baseline 3 — schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb+resource ('Summarize') and details the supported input formats (text, HTML, Markdown, JSON, XML, URL, GitHub raw files). The title and tool name align well. It loses a point because it doesn't explicitly distinguish from closely related siblings like forcedream_extract_action_items or forcedream_extract_data, though the core summarization purpose is clear.

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 implies when to use it (document/URL summarization) but doesn't explicitly state when NOT to use it or name alternative tools. The mention of 'action items' overlaps with forcedream_extract_action_items sibling, which could confuse selection, and no exclusion criteria are given.

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

A4/5.0
Disambiguation3/5

Most tools have clearly distinct purposes (fraud vs extract vs generate vs sentiment vs lead scoring vs quote vs proof verification). However, there is notable overlap among the search_* discovery tools: forcedream_search_agents, forcedream_search_reliability, and forcedream_search_costs all surface overlapping agent metadata (success_rate appears in both search_agents and search_reliability), which could cause misselection. Additionally, forcedream_extract_data vs forcedream_extract_entities vs forcedream_extract_action_items overlap somewhat in the extraction domain despite distinct outputs (JSON fields vs raw entities vs action items).

Naming Consistency4/5

The forcedream_ prefix is used consistently throughout, and most tools follow a forcedream_<verb>_<object> pattern (extract_data, generate_code, score_lead, security_scan). However, there is inconsistency in verb style: check vs extract vs generate vs invoke vs search vs verify vs summarize are all different verb types, and the objects don't follow a uniform noun convention (some are actions like invole_agent, others resources like market_quote). The naming is readable and discoverable but not perfectly uniform.

Tool Count4/5

At 17 tools, this is slightly above the ideal range but justifiable given the broad multi-service scope (fraud, extraction, generation, discovery, verification). Each tool maps to a reasonably distinct service capability, and none feel like padding. The count borders on heavy but earns its place given the diverse domain coverage.

Completeness4/5

The tool surface is comprehensive for a multi-purpose AI/ML service platform, covering fraud detection, data extraction, code generation, sentiment analysis, embeddings, lead scoring, security scanning, summarization, market quotes, agent discovery, and proof verification. Missing are update/delete operations, but this appears to be a stateless service rather than a CRUD resource store. The discovery tools (search_* variants) and meta capabilities (verify_proof) round out the lifecycle well, though there's no clear cleanup or batch-processing tool.