Seitrace Insights MCP Server

Official

by Seitrace

Overview Schema Related Servers Score Discussions

TypeScript

Remote

Server Quality Checklist

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

Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: list_resources enumerates available resources, list_resource_actions enumerates actions for a resource, get_resource_action_schema retrieves schema details, get_resource_action_snippet generates code, and invoke_resource_action executes the action. The descriptions clearly differentiate their functions, eliminating any ambiguity.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case, with verbs like 'get', 'list', and 'invoke' paired with descriptive nouns (e.g., 'resource_action_schema', 'resource_action_snippet'). This uniformity makes the set predictable and easy to understand.
Tool Count5/5
With 5 tools, the set is well-scoped for a server focused on resource action management. Each tool serves a specific role in the workflow (listing, schema retrieval, snippet generation, invocation), and none feel redundant or missing, making the count appropriate for the domain.
Completeness5/5
The tool surface provides complete coverage for interacting with resource actions: listing resources and their actions, retrieving schemas and code snippets, and invoking actions. This covers the full lifecycle from discovery to execution, with no apparent gaps for the server's purpose.
Average 2.7/5 across 5 of 5 tools scored. Lowest: 2/5.
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

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It fails to disclose behavioral traits: it doesn't indicate if this is a read/write operation, what permissions are needed, potential side effects (e.g., data modification), error handling, or response format. The phrase 'invoke' suggests an action execution but lacks details on consequences or safety, leaving critical gaps for an agent.
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 with no wasted words. It is front-loaded and directly addresses the core function, though it lacks depth. While concise, it under-specifies rather than being overly verbose, earning a high score for structure but not full marks due to missing essential details.
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 (3 parameters with nested objects, no annotations, no output schema), the description is incomplete. It doesn't explain what 'invoke' entails, the nature of resources/actions, expected outcomes, or error cases. Without annotations or output schema, the agent lacks crucial information to use the tool effectively, making this inadequate for a general-purpose action tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It mentions 'payload matching its schema,' which adds minimal context about 'payload' but doesn't explain 'resource' or 'action' parameters. With 3 undocumented parameters and no enums, the description fails to provide meaningful semantics beyond what the bare schema (types and required fields) already offers.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Invoke a resource action with a payload matching its schema' restates the tool name ('invoke_resource_action') and title (null) without specifying what 'invoke' means in practice or what types of actions/resources are involved. It distinguishes from siblings like 'get_resource_action_schema' by implying execution rather than retrieval, but remains vague about the actual purpose beyond basic verb+resource.
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 mentions 'payload matching its schema,' which hints at using 'get_resource_action_schema' first, but this is not explicit. There are no exclusions, prerequisites, or clear context for choosing this over sibling tools like 'list_resource_actions' or 'get_resource_action_snippet.'
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 generating a code snippet but doesn't cover critical aspects such as whether this is a read-only operation, if it requires authentication, potential rate limits, error handling, or the format of the output. For a tool with no 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, stating the core purpose in the first sentence. The second sentence provides a helpful example without unnecessary elaboration. However, it could be slightly more structured by explicitly separating parameter explanations, but overall, it avoids waste and is appropriately sized for the tool's complexity.
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 tool's complexity (3 parameters, no annotations, no output schema), the description is incomplete. It lacks details on output format, error cases, dependencies on other tools (e.g., needing to list resources first), and behavioral traits. Without annotations or an output schema, the description should provide more context to fully guide an AI agent, but it falls short.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the schema provides no descriptions for parameters. The description adds minimal semantics by mentioning 'resource, action, and target language' and listing supported languages in the schema, but it doesn't explain what 'resource' or 'action' refer to, their expected formats, or how they interact. This insufficient detail fails to compensate for the low 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's purpose: 'Generate a code snippet to perform a resource action in the specified language.' It includes a specific verb ('Generate') and resource ('code snippet'), and the example adds clarity. However, it doesn't explicitly differentiate from sibling tools like 'invoke_resource_action' or 'get_resource_action_schema', which is why it doesn't achieve 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?
The description provides no guidance on when to use this tool versus alternatives. It mentions an example but doesn't specify contexts, prerequisites, or exclusions relative to siblings like 'invoke_resource_action' (which might execute actions directly) or 'get_resource_action_schema' (which might provide schema details). This lack of comparative guidance limits its utility for an AI agent.
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 states 'List actions' but doesn't describe what an 'action' entails (e.g., operations, permissions, or workflows), how results are returned (e.g., pagination, format), or any limitations (e.g., rate limits, authentication needs). 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: 'List actions for a given resource.' It's front-loaded with the core purpose, has zero wasted words, and is appropriately sized for a simple tool. Every part of the sentence contributes directly to understanding the tool's function.
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 for a tool that lists actions. It doesn't explain what 'actions' are, how they're structured, or what the return value looks like. For a tool with 1 parameter and no structured output information, more context is needed to fully understand its operation and results.
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 parameter 'resource' documented as 'Resource name.' in the schema. The description adds no additional meaning beyond this, such as examples of valid resource names or how they relate to actions. Since the schema already provides basic documentation, the baseline score of 3 is appropriate, but no extra value is added.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'List actions for a given resource' clearly states the verb ('List') and resource ('actions'), but it's vague about what 'actions' means in this context. It doesn't distinguish from siblings like 'list_resources' (which lists resources rather than actions) or 'get_resource_action_schema' (which gets schema details rather than listing). The purpose is understandable but lacks specificity about the nature of these actions.
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 doesn't mention prerequisites, context for selecting this over siblings like 'list_resources' or 'invoke_resource_action', or any constraints. Without such information, the agent must infer usage from tool names alone, which is insufficient for optimal selection.
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 the full burden of behavioral disclosure. It states the tool retrieves a JSON Schema but does not specify whether this is a read-only operation, if it requires authentication, what happens on errors (e.g., invalid resource/action), or the format of the returned schema. This leaves critical behavioral traits unclear for a tool that likely interacts with system resources.
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, clear sentence that directly states the tool's function without any unnecessary words. It is front-loaded and efficiently conveys the core purpose, making it easy to parse and understand 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 tool's complexity (involves specific resources and actions) and the lack of annotations, output schema, and schema descriptions, the description is incomplete. It does not address key contextual aspects like error handling, return format, or dependencies on other tools (e.g., needing to list resources first). For a tool that likely returns structured data, more detail is needed to ensure proper use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the parameters 'resource' and 'action' are undocumented in the schema. The description mentions 'specific resource action' but does not add meaning beyond this, such as examples of valid resources/actions, their formats, or where to find them. This fails to compensate for the schema's lack of documentation, leaving parameters ambiguous.
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 with a specific verb ('Get') and resource ('JSON Schema for a specific resource action'), making it immediately understandable. However, it does not explicitly differentiate from sibling tools like 'get_resource_action_snippet' or 'list_resource_actions', which might also involve resource actions, leaving some ambiguity about its unique role.
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 does not mention prerequisites, such as needing to know the resource and action names beforehand, or contrast it with siblings like 'list_resource_actions' (which might list available actions) or 'invoke_resource_action' (which might execute an action). This lack of context could lead to misuse.
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 for behavioral disclosure. The description only states what the tool does at a high level ('List available resources') but provides no information about what the listing returns (format, structure, completeness), whether there are limitations (pagination, rate limits), or what permissions might be required. For a tool with zero annotation coverage, this is insufficient 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 a single, efficient sentence that gets straight to the point. The parenthetical examples are well-placed and add useful context without unnecessary elaboration. There's zero wasted space - every word serves a purpose in clarifying what the tool does.
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 zero-parameter listing tool with no output schema, the description provides adequate basic information about what the tool does. However, it lacks important context about what the listing returns (format, structure) and how it relates to sibling tools. The examples help, but without annotations or output schema, more behavioral detail would be beneficial for a complete understanding.
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 input schema has 0 parameters with 100% description coverage, so the schema already fully documents that no arguments are required. The description appropriately doesn't waste space discussing parameters that don't exist. The examples provided ('insights_erc20, insights_erc721, insights_native') give helpful context about what types of resources might be listed, which adds value beyond the empty parameter schema.
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 ('List') and resource ('available resources'), and provides concrete examples of what types of resources might be listed. However, it doesn't explicitly differentiate from sibling tools like 'list_resource_actions' - both involve listing, so the distinction between 'resources' and 'resource actions' could be clearer.
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. With sibling tools like 'list_resource_actions' that also perform listing operations, there's no indication of when this tool is appropriate versus when other listing tools should be used. No prerequisites, exclusions, or alternative recommendations are mentioned.
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:

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

Score Badge

Copy to your README.md:

[![seitrace-mcp MCP server](https://glama.ai/mcp/servers/Seitrace/seitrace-mcp/badges/score.svg)](https://glama.ai/mcp/servers/Seitrace/seitrace-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/Seitrace/seitrace-mcp'

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