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VeritasActa

acta-mcp

by VeritasActa

acta_respond

Submit evidence, challenges, updates, or resolutions to existing entries. Each type requires specific fields like source, stance, argument, or outcome for validation.

Instructions

Submit a response to an existing entry. Evidence requires source + stance. Challenges require target_assertion + basis + argument (min 20 chars). Updates require update_type. Resolutions require outcome + source + resolution_type.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyYesResponse text
typeYesResponse type
basisNoRequired for challenges
topicYesTopic slug
sourceNoSource URL (required for evidence, counter_evidence/source_unreliable challenges, and resolutions)
stanceNoRequired for evidence
outcomeNoResolution outcome (required for resolutions)
argumentNoSubstantive refutation (min 20 chars, required for challenges)
target_idYesEntry ID being responded to
provenanceNoOptional AI authorship metadata
update_typeNoRequired for updates
resolution_typeNoRequired for resolutions
target_assertionNoQuote the specific assertion being challenged (min 5 chars, required for challenges)
Behavior3/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It discloses constraints such as minimum character length for 'argument' (min 20 chars) and conditional field requirements (e.g., 'source' required for certain types). However, it does not mention side effects, idempotency, or success/failure responses.

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 concise (one paragraph with multiple sentences) and front-loaded with the primary purpose. It efficiently explains all response types without redundancy. Minor improvement could be structuring as bullet points for readability.

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?

Given the tool's complexity (13 parameters, conditional requirements) and no output schema, the description adequately covers input constraints and selection logic. It does not explain return values or error handling, but these are less critical for selection.

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% (all parameters are described), so baseline is 3. The description adds value by grouping parameters by response type (e.g., 'Challenges require target_assertion + basis + argument'), clarifying conditional requirements beyond what the schema's individual descriptions provide.

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: 'Submit a response to an existing entry.' It further distinguishes itself by detailing four specific response types (evidence, challenge, update, resolution), which are unique from sibling tools like acta_contribute (likely for new entries) and acta_query (for searching).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit requirements for each response type (e.g., 'Evidence requires source + stance'), guiding the agent on when to include each parameter. However, it does not explicitly state when not to use this tool or mention alternatives beyond the implicit differentiation from siblings.

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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