zkproofport-ai

Server Quality Checklist

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Latest release: v0.2.42
Disambiguation5/5
Each tool has a distinct purpose: discovery, all-in-one generation, step-by-step generation (three sequential steps), and verification. The descriptions clearly differentiate generate_proof from the step-by-step trio, and the sequential tools are well-documented with ordering instructions, leaving no ambiguity.
Naming Consistency5/5
All tool names use snake_case with a consistent verb_noun pattern (generate_proof, get_supported_circuits, prepare_inputs, request_challenge, submit_proof, verify_proof). The naming is predictable and follows a logical flow, enhancing usability.
Tool Count5/5
Six tools is well-scoped for a ZK proof server. It covers essential operations: discovery, all-in-one generation, step-by-step generation, and verification. The count is neither too few (missing no critical features) nor too many (each tool is justified).
Completeness4/5
The tool surface covers the core workflow: discover circuits, generate proofs (both simple and step-by-step), and verify on-chain. One minor gap is the lack of proof history or status polling for asynchronous scenarios, but the synchronous step-by-step flow covers the main use case.
Average 4.2/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on May 24, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and discloses execution duration (30-90 sec), internal circuit execution (Noir), and output type (UltraHonk proof). However, it omits error conditions, idempotency, and whether the operation is destructive or reversible.
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 three concise sentences. The first sentence front-loads the purpose and step, the second adds technical detail, and the third covers timing. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and missing annotations, the description does not detail the return format beyond 'UltraHonk proof', nor does it explicitly state prerequisites like the need for a challenge from request_challenge. While it mentions 'prepared inputs', the flow dependencies are implicit.
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 value by linking the 'inputs' parameter to the 'prepare_inputs' sibling tool ('Full ProveInputs object from prepare_inputs'), providing context beyond the schema for one parameter.
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 identifies the tool as Step 3 of a flow, uses a specific verb ('submit prepared inputs'), and specifies the resource ('ZK proof'). It distinguishes from siblings like prepare_inputs (step 2) and verify_proof (step 5) by its position in the sequence.
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 positions the tool in a step-by-step flow ('Step 3') and mentions timing (30-90 sec), but does not explicitly state when to avoid it or contrast with siblings like generate_proof. Usage guidance is implied but not fully explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose all behavioral traits. It states the tool verifies on-chain and returns a success object, but does not mention potential gas costs, network dependencies, failure responses, or side effects.
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 three sentences with no wasted words. It front-loads the purpose and incrementally adds context on how to use the tool and what to expect.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the nested object parameter and lack of output schema, the description effectively covers the main use case and expected return for a valid proof. However, it omits error handling, edge cases, and a full return structure description.
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?
The input schema has 100% coverage with descriptions for all properties. The description adds value by explaining to pass the result object directly, but it reinforces usage rather than adding new semantic meaning 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 action ('Verify a ZK proof on-chain'), the specific resource ('against the deployed verifier contract'), and its place in a workflow ('Step 4 (optional)'). It distinguishes itself from siblings like generate_proof and submit_proof.
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 instructs users to 'Pass the full generate_proof result object directly,' providing clear how-to guidance. It frames the tool as an optional step after proof generation, but lacks explicit when-not-to-use or mention of alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must disclose behavior. It mentions no parameters required but does not state if it's read-only or any side effects. Minimal behavioral info, but acceptable for a simple listing tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no wasted words. Efficiently conveys purpose, output contents, and usage order.
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 has zero parameters and no output schema, the description adequately covers purpose and guidance. Could mention output format or safety of repeated calls, but it's mostly 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?
No parameters defined; description adds value by explicitly stating 'No parameters required,' which reassures the agent. Baseline 4 for zero-parameter tools with coverage.
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?
Clearly states the tool lists all ZK circuits with specific details (verifier addresses and authorized signers). Distinguishes from siblings by being a discovery step before proof generation.
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?
Explicitly says 'Call this first to discover available circuits before starting proof generation,' which guides when to use it. Could be improved by specifying when not to use it or naming alternatives, but it's clear for a discovery tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It explains the network request and that the server returns nonce and TEE key information. Lacks details on side effects, error behavior, or authentication requirements, but covers the core interaction.
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?
Three sentences, no filler. Front-loaded with purpose and flow position. Every sentence 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 low complexity (2 params, no output schema), the description is complete. It covers prerequisite, endpoint, input format, and server response. An agent can invoke this tool correctly with the given information.
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 already describes both parameters with 100% coverage. The description adds value by clarifying that 'inputs' is the full ProveInputs object from prepare_inputs and accepts JSON string or object. This provides critical format and origin context 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 it is Step 2 of a flow and requests a challenge from the server via a specific endpoint. It names the verb 'request' and the resource 'challenge', and positions itself relative to siblings like 'prepare_inputs' and 'generate_proof'.
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?
Explicitly states it must be called after 'prepare_inputs' and specifies the endpoint. Provides clear sequencing but lacks explicit when-not-to-use scenarios or alternatives. However, for a step in a defined flow this is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It describes the tool as a single-call proof generator and mentions the return value, but does not disclose behavioral traits such as authentication requirements, potential side effects, error handling, or any prerequisites (e.g., user login). The description is adequate but lacks depth in 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 concise and well-structured: a one-sentence summary, a usage guideline sentence, a bulleted circuit list, and a returns line. Each part is essential and front-loaded. No redundant phrases.
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 moderate complexity (6 parameters, 3 circuits) and no output schema, the description is thorough. It explains the all-in-one nature, lists circuits with their required inputs, and specifies the return value and the need for separate verification via verify_proof. This covers all key aspects for an agent to use the tool correctly.
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 100% schema coverage, the baseline is 3. The description adds value by grouping parameters per circuit (e.g., 'Requires jwt and scope' for oidc_domain, 'Requires country_list and is_included' for coinbase_country) and noting defaults (scope defaults to 'proofport'). This provides contextual meaning beyond the 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 it is an 'All-in-one ZK proof generation' and lists the three circuits: coinbase_kyc, coinbase_country, and oidc_domain. It explicitly distinguishes the tool from the step-by-step siblings (prepare_inputs, request_challenge, submit_proof), making its purpose unambiguous.
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 guidance: 'Use this when you want the simplest path to a proof. For fine-grained control over each step, use prepare_inputs, request_challenge, and submit_proof individually.' This clearly tells the agent when to use this tool versus alternatives.
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?
Despite no annotations, the description discloses the multi-step process (signing, querying EAS, building Merkle proof) which implies non-trivial behavior. However, it does not mention side effects, idempotency, rate limits, or authentication requirements, leaving some gaps.
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?
A single, well-structured paragraph front-loads the purpose ('Step 1'), then details actions, sequencing, and conditional parameters. Every sentence contributes essential information without redundancy.
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 6 parameters, 3 circuit variants, and no output schema, the description covers the overall process, sequencing, and circuit-specific requirements. However, it lacks a description of the return value structure, which would be helpful for downstream use.
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 parameter descriptions, but the description adds value by explaining conditional usage (e.g., jwt/scope for oidc_domain, is_included/country_list for coinbase_country) and defaults like provider defaulting to 'google'. This reduces ambiguity beyond the schema alone.
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 is step 1 of a step-by-step flow, explicitly listing the actions (compute hash, sign, query EAS, build Merkle proof) and outputs (all inputs for proof generation). It distinguishes between circuit types (oidc_domain vs Coinbase-specific), making the tool's specific role unambiguous.
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 instructs to call this tool BEFORE request_challenge, providing clear sequencing. Also specifies when to provide jwt/scope for oidc_domain circuit versus other parameters, giving contextual usage guidance for different circuits.
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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