Vaultfire MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: discover_agents finds agents by tags, get_agent retrieves identity data, get_bonds lists partnership bonds, get_reputation provides rating data, get_street_cred gives a score and tier, protocol_stats shows system-wide metrics, and verify_agent performs a comprehensive trust check. The descriptions reinforce these unique functions, making misselection unlikely.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with the prefix 'vaultfire_' (e.g., vaultfire_discover_agents, vaultfire_get_agent). This uniform snake_case naming convention makes the set predictable and easy to parse, with no deviations in style or structure across the seven tools.
Tool Count5/5
With 7 tools, the count is well-scoped for a server focused on AI agent verification and data retrieval in the Vaultfire ecosystem. Each tool serves a specific, non-redundant function, covering discovery, identity, bonds, reputation, scoring, stats, and verification, which aligns perfectly with the domain's needs without being overly sparse or bloated.
Completeness5/5
The tool surface provides complete coverage for the domain of AI agent verification and on-chain data access. It includes discovery, detailed identity and bond retrieval, reputation and scoring metrics, protocol-level statistics, and a comprehensive verification tool that integrates all aspects. There are no obvious gaps; agents can perform end-to-end workflows without dead ends.
Average 3.5/5 across 7 of 7 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states the basic action and return type. It doesn't disclose behavioral traits such as whether this is a read-only operation, potential rate limits, authentication needs, or how the on-chain hashing affects results. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
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 a single, efficient sentence that front-loads the purpose and key details. Every word earns its place with no redundancy or unnecessary elaboration, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of querying on-chain data with hashed capabilities and no output schema, the description is incomplete. It doesn't explain the return format (e.g., structure of addresses, pagination), error conditions, or how capability matching works. For a tool with no annotations and no output schema, this leaves too much unspecified for reliable 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 description coverage is 100%, so the schema fully documents both parameters. The description adds no additional meaning beyond what's in the schema, such as explaining the format of capability tags or the implications of chain selection. Baseline 3 is appropriate when the 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 clearly states the verb 'find' and resource 'registered AI agents' with the mechanism 'by capability tags', and specifies the return type 'list of agent addresses'. However, it doesn't explicitly differentiate from sibling tools like 'vaultfire_get_agent' which might retrieve details about a specific agent rather than searching by capabilities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'vaultfire_get_agent' for individual agent details or 'vaultfire_verify_agent' for verification, leaving the agent to infer usage from tool names alone.
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 provided, the description carries the full burden. It discloses that the tool retrieves data (implied read-only) and lists the types of details returned, but lacks information on behavioral traits like rate limits, error handling, authentication needs, or whether it's a query or mutation. It adds some context but is incomplete for a tool with no 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 a single, well-structured sentence that efficiently states the purpose and return details without unnecessary words. It is front-loaded with the main action and provides essential information concisely.
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 annotations and no output schema, the description partially compensates by listing return details, but it does not fully cover behavioral aspects or provide a complete picture for a tool with two parameters. It is adequate but has clear gaps in usage guidance and transparency.
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 description coverage is 100%, so the input schema already documents both parameters ('address' and 'chain') with descriptions and enums. The description does not add any meaning beyond the schema, such as explaining parameter interactions or default behaviors, meeting the baseline for high coverage.
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 clearly states the verb ('Get') and resource ('partnership bonds for an address'), specifying it returns bond IDs and details like partner addresses, type, stake, creation date, and status. However, it does not explicitly differentiate from sibling tools like 'vaultfire_get_agent' or 'vaultfire_get_reputation', which might handle different data types, so it lacks sibling differentiation for a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description does not mention any prerequisites, exclusions, or comparisons to sibling tools such as 'vaultfire_discover_agents' or 'vaultfire_verify_agent', leaving the agent without context for tool selection.
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 provided, the description carries the full burden of behavioral disclosure. It mentions that ratings are 'stored on-chain from real interactions,' which adds context about data source and authenticity, but lacks details on error handling, rate limits, or response format. For a read operation, this is minimally adequate but leaves gaps in understanding operational behavior.
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 highly concise and front-loaded, using a single sentence to convey the core purpose and key metrics. Every word earns its place, with no redundant information. It efficiently communicates the tool's function without unnecessary elaboration, making it easy for an agent to parse quickly.
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 tool's moderate complexity (2 parameters, no output schema, no annotations), the description is somewhat complete but has gaps. It covers what data is retrieved and its on-chain nature, but lacks details on output structure, error cases, or integration with sibling tools. Without annotations or output schema, more context on behavioral aspects would improve completeness for agent 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 description coverage is 100%, so the input schema fully documents the parameters ('address' and 'chain'). The description adds no additional parameter semantics beyond what the schema provides, such as explaining address validation or chain selection implications. Baseline 3 is appropriate as the schema handles the heavy lifting without description enhancement.
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 clearly states the tool's purpose: retrieving reputation data for an AI agent, including specific metrics like average rating, feedback counts, and verification percentages. It distinguishes this from sibling tools like 'vaultfire_get_agent' or 'vaultfire_get_street_cred' by focusing on reputation metrics, though it doesn't explicitly contrast with them. The mention of on-chain storage adds useful context but doesn't fully differentiate from alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'vaultfire_get_agent' or 'vaultfire_get_street_cred', leaving the agent to infer usage based on tool names alone. There's no indication of prerequisites, such as needing an agent address, or when this tool is preferred over others for reputation-related queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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. While it mentions the return format ('ERC-8004 compliant identity info'), it lacks critical operational details: whether this is a read-only operation (implied but not stated), authentication requirements, rate limits, error conditions, or what happens with invalid addresses. The description provides basic functionality but misses important 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 perfectly concise - two sentences that efficiently communicate purpose and return format with zero wasted words. The first sentence clearly states what the tool does and what data it returns, while the second provides important compliance context. Every element earns its place.
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?
For a read operation with 2 parameters and 100% schema coverage but no output schema, the description provides adequate but incomplete context. It clearly states what data is returned but doesn't describe the response structure or format details. The ERC-8004 compliance mention is helpful but assumes prior knowledge. Given the missing output schema, more detail about return values would improve completeness.
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 description coverage is 100%, so the schema already fully documents both parameters. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain format requirements for the address beyond '0x...' or clarify chain selection implications. With complete schema coverage, the baseline score of 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 the specific action ('Get on-chain identity data') and resource ('for an agent address'), listing exactly what data is retrieved (agent URI, agent type, registration timestamp, active status). It distinguishes itself from siblings like vaultfire_get_bonds or vaultfire_get_reputation by focusing on core identity information rather than financial or social metrics.
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 usage when identity data is needed for an agent address, but provides no explicit guidance on when to choose this tool versus alternatives like vaultfire_verify_agent (which might verify rather than retrieve) or vaultfire_discover_agents (which might list multiple agents). No when-not-to-use scenarios or prerequisites are mentioned.
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 provided, the description carries the full burden of behavioral disclosure. It describes the output (score, tier, breakdown) but doesn't mention potential errors, rate limits, authentication needs, or data freshness. It adds some context by detailing the tier ranges and score components, but lacks operational traits like response format or failure modes.
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 front-loaded with the core purpose in the first sentence, followed by tier details and additional output information. Every sentence adds value (e.g., tier ranges, breakdown explanation) without redundancy, making it efficient and well-structured for quick comprehension.
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 moderate complexity (2 parameters, no output schema, no annotations), the description is reasonably complete. It explains the output (score, tier, breakdown) and tier ranges, which compensates for the lack of output schema. However, it could improve by addressing behavioral aspects like error handling or data sources, leaving minor gaps.
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 schema description coverage is 100%, so the input schema already documents both parameters (address and chain) thoroughly. The description doesn't add any parameter-specific details beyond what the schema provides, such as examples or edge cases, but it doesn't need to compensate for gaps, resulting in a baseline score of 3.
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 with specific verbs ('Get the Street Cred score and tier') and resource ('for an agent address'), distinguishing it from siblings like vaultfire_get_agent or vaultfire_get_reputation by focusing on a specific scoring metric. It explicitly mentions the score range (0-95) and tier breakdown, making the 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying it's for an agent address, but it doesn't explicitly state when to use this tool versus alternatives like vaultfire_get_agent or vaultfire_get_reputation. There's no guidance on prerequisites, exclusions, or comparative contexts, leaving the agent to infer usage from the purpose alone.
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 carries the full burden. It discloses the tool's read-only nature ('Get') and the specific metrics returned, which is helpful. However, it lacks details on behavioral traits like rate limits, authentication requirements, error conditions, or response format, which would be valuable for a tool with no annotation coverage.
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 a single, well-structured sentence that front-loads the purpose and efficiently lists the four key statistics. Every word contributes to understanding the tool's function without redundancy or fluff, making it highly concise and effective.
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 low complexity (one optional parameter, no output schema, no annotations), the description is reasonably complete. It clearly states what statistics are returned, which compensates for the lack of output schema. However, it could improve by mentioning response format or any limitations, but for a simple read operation, it provides sufficient context.
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% description coverage, with the 'chain' parameter fully documented (type, enum values, default). The description adds no parameter-specific information beyond what the schema provides, such as clarifying the 'native token' unit or 'bridge sync' meaning. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't enhance parameter understanding.
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 specific action ('Get') and resource ('Vaultfire Protocol statistics'), listing the exact metrics returned (total registered agents, total bonds created, total active bond value, bridge sync count). It distinguishes this from sibling tools like 'vaultfire_get_agent' or 'vaultfire_get_bonds' by focusing on aggregate protocol-level stats rather than individual entities.
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 usage for retrieving protocol-wide statistics, but provides no explicit guidance on when to use this tool versus alternatives like 'vaultfire_discover_agents' or 'vaultfire_get_bonds'. No exclusions or prerequisites are mentioned, leaving the agent to infer context from the tool name and description alone.
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 carries the full burden. It discloses behavioral traits such as returning a 'trusted/untrusted verdict with reason' and performing multiple checks, but lacks details on rate limits, error handling, or authentication needs. The description is informative but not comprehensive for a verification tool with no annotation support.
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 front-loaded with the tool's purpose and key checks, followed by usage guidance. Every sentence earns its place by adding value, with no redundant or vague language, making it efficient and well-structured.
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 complexity of a verification tool with 3 parameters and no output schema, the description is mostly complete. It explains what the tool does and when to use it, but lacks details on output format or behavioral aspects like error cases. With no annotations, it could benefit from more transparency, but it's adequate for basic 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 description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional parameter semantics beyond what the schema provides, such as explaining interactions between parameters or usage nuances. Baseline 3 is appropriate as the schema does the heavy lifting.
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 with specific verbs ('verify', 'checks') and resources ('AI agent address'), listing multiple verification criteria (on-chain identity, partnership bonds, Street Cred score, reputation data, cross-chain bridge recognition). It distinguishes from sibling tools like 'vaultfire_get_street_cred' or 'vaultfire_get_reputation' by combining all checks into a single trusted/untrusted verdict.
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 explicitly states when to use this tool: 'Use this before interacting with an unknown agent.' This provides clear context for usage versus alternatives, though it doesn't name specific sibling tools, the guidance is direct and actionable for the agent's decision-making.
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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