x402 crypto market structure

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.0
Disambiguation4/5
Most tools have distinct purposes, such as addressRisk for wallet analysis and apiInfo for API details, but the market-related tools (marketAnalyze, marketFull, marketOrderflow, marketSnapshot) have some overlap in providing market data, which could cause confusion. However, their descriptions help clarify specific focuses like orderflow vs. snapshot.
Naming Consistency3/5
The naming is mixed: history tools use a consistent pattern (history1d, history1h, history5m), but market tools vary (marketAnalyze, marketFull, marketOrderflow, marketSnapshot), and others like addressRisk and apiInfo use different styles. This lacks a unified verb_noun convention, though names remain readable.
Tool Count5/5
With 9 tools, the count is well-scoped for a crypto market structure server, covering risk assessment, API info, historical data at multiple resolutions, and various market analyses. Each tool earns its place without feeling excessive or insufficient.
Completeness4/5
The tool set covers key aspects of crypto market analysis, including risk, API access, historical data, and real-time market insights. Minor gaps might exist, such as lack of tools for portfolio management or advanced trading actions, but core workflows for data retrieval and analysis are well-covered.
Average 3.3/5 across 9 of 9 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 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.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 for behavioral disclosure. While it mentions the analysis outputs, it doesn't describe execution characteristics like whether it's read-only, requires authentication, has rate limits, or what happens on failure. For a tool with no annotation coverage, this is a significant gap.
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 a single, efficient sentence that lists all analysis components. It's appropriately sized and front-loaded with the main purpose, though it could be slightly more structured by separating the analysis components for better readability.
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 complexity of market analysis, no annotations, and no output schema, the description is moderately complete. It outlines what the analysis includes but lacks details on return format, error conditions, or behavioral constraints that would be needed for full contextual understanding.
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 description doesn't mention any parameters, but the input schema has 100% coverage with one parameter (token) fully documented. The baseline is 3 since the schema does the heavy lifting, though the description could have added context about why token selection matters for the analysis.
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 performs 'Full pre-trade macro analysis' with specific components listed (regime detection, DXY, VIX, fear/greed, directional signal and confidence score). It specifies the verb 'analyze' and resource 'market' context, though it doesn't explicitly differentiate from sibling tools like marketFull or marketSnapshot.
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 like marketFull, marketOrderflow, or marketSnapshot. It mentions 'pre-trade' context but doesn't specify use cases, prerequisites, or exclusions relative to sibling tools.
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?
No annotations are provided, so the description carries full burden. It mentions 'real-time' data but doesn't disclose behavioral traits like rate limits, authentication requirements, data freshness guarantees, or whether this is a read-only operation. For a real-time data tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.
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 extremely concise - a single sentence that efficiently communicates the core functionality. Every word earns its place by specifying the data type, key metrics, and scope. There's no wasted verbiage or unnecessary elaboration.
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?
For a real-time data tool with no annotations and no output schema, the description is insufficient. It doesn't explain what format the data returns, how frequently it updates, whether there are subscription requirements, or what 'across exchanges' means operationally. The description leaves too many practical questions unanswered for effective tool invocation.
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%, with the single parameter 'token' clearly documented in the schema. The description doesn't add any parameter-specific information beyond what the schema provides (it doesn't explain token format constraints or default behavior). With high 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides 'real-time orderflow data' with specific metrics (buy/sell pressure, delta, imbalance) across exchanges. It uses a specific verb ('provides' implied) and resource (orderflow data), but doesn't explicitly distinguish from sibling tools like marketAnalyze or marketSnapshot, which might offer different market data.
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 prerequisites, timing considerations, or compare it to sibling tools like marketAnalyze or marketSnapshot that might offer different types of market data. The agent must infer usage from the 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.
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. It mentions 'bundle' and 'combined' but doesn't specify whether this is a read-only operation, what data format is returned, whether there are rate limits, authentication requirements, or any side effects. For a data retrieval tool with zero annotation coverage, this leaves significant behavioral questions unanswered.
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 extremely concise with just two short phrases. It's front-loaded with the core purpose. While efficient, it might be too brief given the lack of annotations and behavioral context needed for a comprehensive data tool.
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 this is a market data tool with no annotations, no output schema, and siblings that provide similar functionality, the description is insufficient. It doesn't explain what 'combined' means operationally, what data format to expect, or how this differs from using marketSnapshot and marketOrderflow separately. For a tool positioned as 'most comprehensive view,' more context is needed.
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% with the single parameter 'token' well-documented in the schema. The description doesn't add any parameter-specific information beyond what the schema provides. According to guidelines, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description, which applies here.
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 provides 'Full market data bundle: snapshot + orderflow combined' which specifies the verb (provides) and resource (market data bundle). It distinguishes from siblings by mentioning 'snapshot + orderflow combined' which differentiates it from marketSnapshot and marketOrderflow. However, it doesn't explicitly state what 'combined' means operationally.
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 context with 'Most comprehensive view' suggesting this should be used when needing complete market data. However, it doesn't explicitly state when to use this versus marketSnapshot or marketOrderflow, nor does it provide any exclusion criteria or prerequisites. The guidance is implied rather than explicit.
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions what the tool flags (mixers, sanctions, high-risk counterparties) but doesn't describe how the risk score is calculated, what format the output takes, whether it's real-time or cached, rate limits, or error handling. For a risk assessment 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 extremely concise and front-loaded, consisting of a single sentence that efficiently communicates the core functionality. Every word earns its place, with no redundant or vague language, 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 risk assessment (involving scoring and flagging multiple risk types) and the absence of both annotations and an output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., score range, flag details), how to interpret results, or behavioral aspects like data freshness or limitations. For a tool with no structured output documentation, this leaves too much undefined.
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 'address' parameter fully documented in the schema. The description doesn't add any parameter-specific details beyond what the schema provides (e.g., it doesn't clarify address validation rules or examples). With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.
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 ('Risk score', 'Flags') and resources ('Ethereum wallet address'), distinguishing it from sibling tools focused on market data, history, or API info. It precisely communicates what the tool does: assess risk and identify specific risk categories.
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. While it implies usage for risk assessment of Ethereum addresses, it doesn't mention prerequisites, exclusions, or compare it to sibling tools like marketAnalyze or history tools that might offer different data. No explicit when/when-not instructions are present.
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?
No annotations are provided, so the description carries the full burden. It mentions 'high-resolution intraday data' which hints at detailed data, but fails to disclose critical behavioral traits such as rate limits, data freshness, authentication needs, or what 'OHLCV' specifically entails (e.g., open, high, low, close, volume). This leaves significant gaps for an agent to understand operational constraints.
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 extremely concise with two short phrases: '5-minute OHLCV price bars. High-resolution intraday data.' Every word earns its place by specifying timeframe, data type, and resolution without any waste or redundancy.
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 financial data tools and the lack of annotations and output schema, the description is incomplete. It doesn't explain return values (e.g., structure of OHLCV bars), data sources, latency, or error handling. For a tool with no structured output and behavioral gaps, this is inadequate for an agent to use effectively.
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%, with clear descriptions for 'token' and 'limit' parameters. The description adds no additional parameter semantics beyond what the schema provides, such as format details for 'token' or constraints on 'limit'. Baseline 3 is appropriate since the schema adequately documents parameters.
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 provides '5-minute OHLCV price bars' and 'high-resolution intraday data', which specifies the verb (provides/returns) and resource (price bars/data). It distinguishes from siblings like history1d and history1h by specifying the 5-minute timeframe, though it doesn't explicitly name those alternatives.
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 intraday data analysis by mentioning 'high-resolution intraday data', suggesting it's for short-term timeframes. However, it doesn't provide explicit guidance on when to use this tool versus alternatives like history1h or history1d, nor does it mention any exclusions or prerequisites.
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?
No annotations are provided, so the description carries the full burden. It mentions 'Live crypto market snapshot' which suggests real-time data, but does not disclose behavioral traits such as rate limits, data freshness, authentication needs, or error handling. The description is minimal and lacks critical operational details.
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 appropriately sized and front-loaded, starting with the core purpose and followed by supported tokens. It uses a single sentence efficiently, though it could be slightly more structured for readability.
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 lack of annotations and output schema, the description is incomplete. It does not explain what the return values look like (e.g., format of the snapshot data), error conditions, or other contextual details needed for effective tool use. The description relies too heavily on implied understanding.
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 the 'token' parameter. The description adds value by listing specific token examples (BTC, ETH, SOL, etc.), which helps clarify valid inputs beyond the schema's generic description. However, it does not provide additional syntax or format details.
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 ('Live crypto market snapshot') and resources (price, funding rate, open interest, etc.), and distinguishes it from siblings by listing the specific tokens it supports. It provides a comprehensive overview of what data the tool retrieves.
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 listing the supported tokens, but does not explicitly state when to use this tool versus alternatives like marketAnalyze or marketFull. It provides some context but lacks explicit guidance on tool selection or exclusions.
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 describes the tool as informational ('pricing, quick start guide, and migration details'), which suggests a read-only, non-destructive operation. However, it lacks details on authentication requirements, rate limits, response format, or error handling, which are important for behavioral transparency.
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 key information ('API pricing, quick start guide, and migration details') without unnecessary words. Every part of the sentence contributes to understanding the tool's purpose, making it appropriately concise and well-structured.
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 complexity (simple informational with 0 parameters), no annotations, and no output schema, the description is minimally adequate. It covers the purpose but lacks details on output format, error cases, or behavioral constraints. For a tool with no structured fields to rely on, it should provide more context to be fully 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?
The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the absence of inputs. The description doesn't need to add parameter semantics, and it doesn't incorrectly imply any parameters. A baseline of 4 is appropriate for a zero-parameter tool with complete schema 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 tool provides 'API pricing, quick start guide, and migration details' for the 'x402 API' and 'pay-per-call REST endpoint', which is a specific verb (provides) and resource (API documentation). However, it doesn't explicitly differentiate from sibling tools like 'marketAnalyze' or 'marketSnapshot', which appear to be market data tools rather than API documentation tools.
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 implies usage for API information needs but provides no explicit guidance on when to use this tool versus alternatives. There's no mention of prerequisites, context for use, or comparison with sibling tools, leaving the agent to infer usage based on 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the tool returns historical data (implied read-only), specifies data granularity (1-hour bars), and mentions constraints (up to 7 years, max 5,000 bars per call). However, it lacks details on error handling, rate limits, authentication needs, or response format, which are important for a tool with data retrieval.
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 extremely concise and front-loaded, consisting of just two sentences that efficiently convey the tool's purpose and key constraints. Every word earns its place, with no redundant or vague language, making it easy for an AI agent to quickly understand the tool's functionality.
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 complexity (historical data retrieval with two parameters) and lack of annotations and output schema, the description is moderately complete. It covers the data type, granularity, and constraints but omits details on response format, error conditions, and authentication requirements. For a tool without structured output documentation, more context on what the tool returns would be beneficial to achieve a higher score.
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 both parameters (token and limit) well-documented in the schema. The description adds no additional parameter semantics beyond what the schema provides, such as explaining token symbol formats or bar count implications. Given the high schema coverage, a baseline score of 3 is appropriate as the description does not compensate but also does not detract.
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 ('Hourly OHLCV price history with buy/sell flow data') and distinguishes it from siblings by specifying the time granularity ('1-hour bars'). It explicitly mentions the resource (price history data) and scope (up to 7 years, up to 5,000 bars per call), making it easy to differentiate from tools like history1d or history5m.
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 clear context for when to use this tool by specifying the data type (hourly OHLCV with buy/sell flow) and limitations (up to 7 years, max 5,000 bars). However, it does not explicitly state when not to use it or name alternatives (e.g., history1d for daily data or history5m for 5-minute data), which would be needed for a score of 5.
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 adds useful context beyond the schema, such as the 7-year data limit and 5,000-bar cap per call, which are behavioral traits. However, it lacks details on error handling, rate limits, or data freshness, leaving gaps for a mutation-free 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?
The description is appropriately sized and front-loaded, with three concise sentences that each add value: the first defines the data, the second sets constraints, and the third suggests use cases. There is no wasted text, 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 tool's complexity (read-only data retrieval with two parameters), no annotations, and no output schema, the description is mostly complete. It covers purpose, constraints, and use cases, but lacks details on output format or error scenarios, which would be helpful for full 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?
The schema description coverage is 100%, so the schema already documents both parameters (token and limit) fully. The description does not add any parameter-specific semantics beyond what the schema provides, such as examples or format details, resulting in the 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 and resources: 'Daily OHLCV price history with buy/sell flow data' identifies what data is retrieved, and 'Up to 7 years of daily bars, up to 5,000 bars per call' specifies the scope and constraints. It distinguishes from siblings like history1h and history5m by emphasizing daily frequency.
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 clear context for when to use this tool: 'Good for backtesting and trend analysis' suggests appropriate use cases. However, it does not explicitly state when not to use it or name alternatives (e.g., history1h for hourly data), which prevents a score of 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![x402-mcp MCP server](https://glama.ai/mcp/servers/tunedforai/x402-mcp/badges/card.svg)](https://glama.ai/mcp/servers/tunedforai/x402-mcp)

Score Badge

Copy to your README.md:

[![x402-mcp MCP server](https://glama.ai/mcp/servers/tunedforai/x402-mcp/badges/score.svg)](https://glama.ai/mcp/servers/tunedforai/x402-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/tunedforai/x402-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server