Skip to main content
Glama
yar822

tv-history

by yar822
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a clearly distinct output: asset_analysis provides derived market analysis, asset_bars returns raw OHLCV data, and asset_chart renders a visual PNG chart. Although they share similar inputs, the purpose of each is unambiguous and there is no functional overlap.

    Naming Consistency5/5

    All three tools follow the identical asset_<resource> noun pattern (analysis, bars, chart). This is a clean, consistent convention that makes the tool's purpose predictable from its name.

    Tool Count5/5

    With only three tools, the server is tightly scoped to its stated purpose of providing historical market data, analysis, and visualization. Each tool earns its place, and the count is not overly thin or excessive for the domain.

    Completeness5/5

    For a read-only history server, the tool set covers the core needs: raw data retrieval (asset_bars), derived analysis (asset_analysis), and visual representation (asset_chart). There are no obvious dead ends or missing lifecycle operations, as the domain does not involve creating or modifying data.

  • Average 4.4/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 10 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

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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

  • Behavior5/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries the full burden and exceeds expectations. It discloses detailed behaviors: bar finalization rules, source confirmation and exchange delays, use of finalized bars for derived metrics, output contents, and numeric precision. This is rich, non-redundant context.

    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 organized with a clear input list followed by behavioral details. While longer than strictly necessary, each sentence provides useful information and the structure makes it scannable. Slightly verbose but not wasteful.

    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?

    For a complex tool with 5 parameters, no output schema, and no annotations, the description is remarkably complete. It covers input semantics, defaults, timing behavior, output components, and precision. The agent has enough context to invoke the tool correctly and understand the response shape.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Despite the schema having 0% description coverage, the description thoroughly documents every parameter: asset format and accepted variations, timeframe enum values with default, timestamp meaning and default, response_version const, and include_indicators effect. This adds significant value beyond the bare 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 tool returns market analysis at a requested time, which is a specific verb+resource. It distinguishes itself from sibling tools by focusing on analysis output (indicators, structure, levels) rather than raw bars or charts, though it does not explicitly name the siblings.

    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 context on how the tool behaves (e.g., timestamp cutoff) but gives no explicit guidance on when to choose this tool over asset_bars or asset_chart. No alternatives are mentioned, so the agent must infer usage from the listed output capabilities.

    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?

    No annotations are provided, so the description carries full responsibility. It discloses non-obvious behaviors: only confirmed bars are rendered, exchange-specific delay affects completion, sessions takes precedence over days, and the return includes metadata fields. This adds value beyond the schema and gives the agent a realistic model of tool 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 well-structured: a one-line purpose, a bullet-like input list, and a separate paragraph for rendering behavior and return metadata. Every sentence adds functional detail without filler. It 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.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given there is no output schema and no annotations, the description covers everything needed: input semantics, precedence rules, rendering logic, and a comprehensive list of return metadata fields. It fully compensates for the missing structured information.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema coverage is 0%, so the description must explain all parameters. It does so thoroughly: asset format (EXCHANGE:SYMBOL preferred, legacy SYMBOL:EXCHANGE accepted), timeframe enum values, timestamp ISO-8601, days range 1-365, sessions meaning, and response_version constraint. This is essential guidance that the schema alone would not provide.

    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 returns a PNG candlestick and volume chart ending at a requested time. This specific verb-resource combination distinguishes it from siblings like asset_bars (likely raw price data) and asset_analysis (likely analytical output).

    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 gives clear context for when the tool is appropriate (chart generation with specific parameters) but does not explicitly mention alternatives or when not to use it. Sibling names are visible but not referenced, so the usage 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.

  • Behavior5/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations, the description carries the full transparency burden. It discloses ordering, cutoff eligibility, incomplete-bar handling, return fields including ATR-14 and gap quality, and the delay-based completeness logic. This is far beyond minimal disclosure.

    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 structured with labeled parameter bullets and a return summary. Every sentence carries useful information, with the core purpose front-loaded. It is appropriately detailed without being redundant.

    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 five parameters, no output schema, and no annotations, the description is remarkably complete. It explains all input semantics, the shape of the returned objects, and edge cases like incomplete bars and session-based counting.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, yet the description fully compensates by documenting every parameter: preferred asset format, timeframe enum values, timestamp cutoff semantics, count direction and range, and sessions precedence. This adds meaning well beyond the raw 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 opens with 'Return raw OHLCV bars, ordered oldest-first', a specific verb+resource combination. The word 'raw' distinguishes it from sibling tools like asset_analysis and asset_chart, making its purpose unmistakable.

    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 gives clear contextual guidance: it returns raw bars in a specified window, and explains parameter precedence such as sessions overriding count. It does not explicitly name alternatives or state when not to use this tool, but the contrast with siblings is implicit.

    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

tv_history_mcp MCP server

Copy to your README.md:

Score Badge

tv_history_mcp MCP server

Copy to your README.md:

Latest Blog Posts

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/yar822/tv_history_mcp'

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