dd-explain
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The two tools take entirely different inputs (a txid vs. OP_RETURN hex) and serve distinct purposes: high-level transaction explanation versus low-level script decoding. Their descriptions clearly delineate when each should be used, leaving no ambiguity.
Naming Consistency5/5Both tools follow the same verb_noun snake_case pattern: explain_digidollar_tx and decode_dd_opreturn. The verbs accurately describe the action and the nouns specify the target, making the naming predictable and consistent.
Tool Count3/5With only two tools, the server feels thin even though its purpose is narrowly focused on explaining DigiDollar transactions. The count is borderline but acceptable for such a specialized utility; it is not excessive but also not richly scoped.
Completeness4/5The tool surface covers the essential workflows for its domain: full transaction explanation and offline OP_RETURN decoding. Minor gaps exist, such as no support for raw full-transaction hex decoding, but the core explainer and decoder are sufficient for most use cases.
Average 4.1/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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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
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does so thoroughly: it discloses fixture vs live resolution, the ALLOW_NETWORK gating, the unverified-fixture caveat, read-only behavior, and that inferences are marked Unverified. It also explains the burn calculation rather than hiding it.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is dense but information-rich and front-loaded with the core purpose before details. The product name and lookup UI are useful context but slightly peripheral for an agent selecting/invoking the tool, keeping it from a perfect conciseness score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having no output schema, the description lists the returned fields and caveats in enough detail to set expectations. It could be more complete about error behavior (e.g., missing fixture with ALLOW_NETWORK unset) and about how it relates to decode_dd_opreturn, but it is mostly sufficient for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already covers txid well ('64-hex DigiByte transaction id'), so schema coverage is 100%; baseline is 3. The description adds little about parameter formatting beyond referring to 'txid' and the TXID.json filename, which does not materially extend the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The opening phrase names a specific verb and resource ('Explain a DigiDollar transaction ... from its txid') and lists distinctive outputs, so the function is clear. It does not explicitly contrast with the sibling decode_dd_opreturn, so it falls short of full sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides operational context (fixture-first, live only when ALLOW_NETWORK=1) and a warning about fixtures, but it never states when to choose this over decode_dd_opreturn or when not to use it. Usage is therefore mostly implied rather than explicitly routed.
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?
With no annotations provided, the description carries the full disclosure burden and does well: it explicitly states no network access, offline execution, read-only behavior, and the output scope limitation (no entity labeling). It does not cover error behavior on malformed hex, which is a minor gap for a decode tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Three short sentences, all informative: core purpose, return values, and behavioral constraints. The purpose is front-loaded and every sentence earns its place with zero redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter offline decode tool with no output schema, the description covers purpose, return contents (kind, amounts in cents, fields), and behavioral traits. It is somewhat vague on what 'and fields' means and does not address invalid-input handling, but nothing essential is missing for an agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the single parameter scriptPubKeyHex is already documented as an OP_RETURN scriptPubKey in hex typically starting with 6a. The description adds no parameter-specific detail beyond what the schema already provides, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('decode'), a specific resource (DigiDollar OP_RETURN scriptPubKey hex), names the implementation path (parseDDOpReturn), and spells out the return values: consensus type byte kind (mint/transfer/redeem), amounts in cents, and fields. The closing 'Does not label entities' differentiates it from the sibling explain_digidollar_tx.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description establishes clear usage context: a quick offline decode of scriptPubKey hex with no network dependency and read-only guarantees. It gives a negative signal ('Does not label entities') but stops short of explicitly naming explain_digidollar_tx as the alternative or stating when to prefer one over the other.
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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