tradingview-mcp

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Each tool targets a distinct surface—screening, TA ratings, symbol lookup, install diagnostics, provider health, and OHLCV data—so most are unambiguous. The two diagnostic tools have some overlap around environment/credential health but are separated by install prerequisites versus current data-feed availability.
Naming Consistency3/5
All names share a tv_ prefix and snake_case, but the internal pattern is mixed: action-style names like tv_screener_run and tv_data_get_bars sit beside noun-style names like tv_ta_summary, tv_symbol_search, tv_setup_doctor, and tv_data_providers_status. A consistent verb_noun or noun_verb pattern would make the set more predictable.
Tool Count5/5
Six tools is well-scoped for a TradingView-oriented data server: screening, symbol discovery, technical summaries, historical bars, and health diagnostics each have a home. No tool feels redundant, and the count is neither thin nor bloated.
Completeness4/5
Core workflows are covered: find symbols, screen/filter, get TA reads, and pull OHLCV bars, plus status and diagnostic tools. Minor gaps exist—such as no direct single-symbol quote tool separate from bars/screener and no explicit indicator-value fetch beyond the TA summary—but agents can work around these.
Average 4.4/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 5 commits in the last 12 weeks
- Last stable release on August 27, 2026
- 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.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description adds that results are exchange-qualified tickers with descriptions/types, but it does not disclose rate limits, pagination behavior, or any other operational traits beyond what annotations and the output schema would imply.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences with no filler. The action and resource are front-loaded, and the return contents fit in a compact clause, making the definition easy to scan and act on.
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?
For a simple read-only symbol search with an output schema and clear annotations, the description is largely sufficient. It could mention when to prefer this over tv_screener_run, but the core invocation context is adequately covered.
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 fully documents the 'text' parameter with examples, leaving 'limit' without a semantic description. The tool description itself does not compensate by explaining limit or its role in result size, although the parameter name and min/max/default constraints make its meaning fairly inferable.
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 uses a specific verb and resource: 'Search TradingView's symbol directory.' It also states the output shape ('exchange-qualified tickers with descriptions and types'), which clearly differentiates it from the screened runs, TA summaries, and data bar tools listed as siblings.
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 makes the intended use clear: an agent should call this when a user wants to find symbols in TradingView's directory. It does not explicitly name alternatives or exclusions, but the verb and resource are specific enough to disambiguate from the sibling tools.
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?
Annotations already mark the tool as readOnlyHint=true and openWorldHint=true, so the description adds value by revealing that results are time-sensitive ('right now') and that the tool will explain the cause of unavailability. It does not contradict annotations and provides behavioral context beyond the structured fields.
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, tightly worded sentence that immediately communicates purpose and scope. Every word earns its place, and the core question ('which providers are usable') is front-loaded before the explanatory clause.
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 zero-parameter, read-only status tool with an output schema and annotations already covering safety, the description is complete. It tells the agent what the tool does, what makes it different from data-fetching tools, and what kind of answer to expect (usable or not, plus reasons).
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?
This tool has zero parameters and an empty input schema, so there are no parameter semantics for the description to clarify. Per the rubric, a zero-parameter tool earns a baseline of 4; nothing is missing here.
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: checking which OHLCV data providers are currently usable, and provides the additional promise of explaining why not if a provider is unavailable. This is specific and easily distinguishes it from sibling tools like tv_data_get_bars or tv_screener_run, which serve different functions.
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 a real-time availability check but does not explicitly state when to use this tool versus siblings, nor does it mention any exclusions or alternatives. An agent can infer use context, but there is no direct guidance such as 'check this before requesting data' or 'use tv_data_get_bars for historical bars instead.'
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?
Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds valuable behavioral context by disclosing that ratings are TradingView's precomputed Recommend.* fields and explaining the mapping to STRONG_BUY..STRONG_SELL.
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 compact and front-loaded: the first sentence states what the tool returns, the second explains the rating source and mapping, and the third gives a clear usage caveat. Every sentence contributes useful information with no filler.
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?
With a complete input schema, an output schema present, and annotations covering read-only behavior, the description adds the remaining essential context: data provenance, rating mapping, and the caution that this is not a trade signal. Nothing critical is missing for an agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% and each parameter already has a meaningful description, including examples and auto-prefix behavior for symbols. The tool description does not add parameter-specific detail beyond what the schema provides, so 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 tool produces a technical-analysis summary per symbol, naming the specific components: overall/MA/oscillator ratings plus RSI and close. It also explains the rating values are TradingView Recommend.* fields mapped to STRONG_BUY..STRONG_SELL, which distinguishes this from sibling tools like tv_data_get_bars and tv_screener_run.
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 explicitly says to use it for a quick bias read and not as a trade signal, giving clear context and an exclusion. It does not name a specific alternative tool, but the usage context is still well-defined.
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?
Annotations declare readOnlyHint and openWorldHint, and the description adds substantial context: Parquet caching makes repeat queries instant, first fetch can be slow, bars are UTC, output format is arrays, and feeds disagree with feed-specific levels. This goes beyond the annotation hints without contradicting them.
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 compact, with each sentence serving a purpose: main action, caching, providers, output format, and a caveat. It is front-loaded with the main verb and resource, and no filler.
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?
With an output schema present, the description still covers key behavioral aspects: caching, provider specifics, timezone, output format, and feed disagreement. For a 6-parameter tool, this is complete enough for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers 100% of parameters, so the baseline is 3. The description adds meaning to the provider parameter by explaining provider differences (dukascopy free/deep, oanda requires key), and clarifies the output format. This extra context justifies a 4.
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 states a specific verb ('Fetch') and resource ('historical OHLCV bars'), and the provider/output details clearly differentiate it from screening, TA, and search tools. The purpose is 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?
No explicit guidance on when to use this tool versus siblings is provided. However, the description implies it is the go-to for historical bar data, and provider selection is covered. Since alternatives are not named, it only achieves implied usage.
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?
Annotations already declare read-only and open-world behavior, so the bar is lower. The description still adds meaningful context beyond annotations: the scanner is unauthenticated, US-equity quotes are 15-minute delayed, forex/crypto are near-realtime, and timeframe-scoped fields use a suffix like 'RSI|15'. This helps set expectations without contradicting 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 compact and front-loaded: the core operation comes first, followed by concrete use cases, then important data-quality caveats. Each sentence contributes distinct value with no redundancy.
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 tool with zero required parameters, full schema coverage, clear annotations, and an output schema, the description covers the key operational nuances: when to use it, field flexibility, data delay caveats, and field-name formatting. Nothing essential is missing for an agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds beyond the schema by explaining the timeframe-suffix convention for fields (e.g., 'RSI|15') and signaling the wide field space (~3000 fields), which helps the agent construct valid columns and filters.
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?
Description opens with a specific verb and resource: 'Run a TradingView screener query'. It further differentiates from siblings by scoping to ranking/filtering instruments, oversold pairs, and volume leaders, which clearly separates it from symbol search, bar retrieval, and TA summary tools.
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 an explicit 'Use for' list covering ranking, filtering, identifying oversold pairs, and volume leaders. It does not name alternatives or state when not to use the tool, but the use cases are clear enough for an agent to select it over siblings.
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 readOnlyHint=true, the annotation already indicates safety, but the description goes well beyond that by disclosing the exact output shape ({name, ok, detail, optional}), the presence of a `fix` field, and the required workflow after running fixes. It also transparently states that credential checks always require a human and must not be attempted automatically. This is rich behavioral disclosure that complements the annotations without contradicting them.
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 compact and front-loaded with the core purpose. Each sentence adds distinct value: first the diagnose-and-fix purpose, then the output contract, then the remediation workflow, and finally the credential-handling caveat. There is no filler or repetition.
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 zero-parameter, read-only diagnostic tool, the description covers everything an agent needs: what the tool does, what it returns, how to use the results, and which actions require human involvement. The presence of an output schema reduces the need to document return values further, and the description already summarizes the key output fields.
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 zero parameters, so there is nothing for the description to explain about parameter usage. The description instead focuses on the diagnostic workflow and output contract, which is the most relevant semantic content for this tool. The baseline of 4 for no-parameter tools 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 opens with a concrete action: 'Diagnose this install: check every prerequisite, return exact fixes.' This clearly identifies the tool as a setup diagnostic, distinguishing it from siblings like tv_screener_run or tv_data_get_bars, which perform different functions. A specific resource ('this install') and expected outcome ('return exact fixes') makes 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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit workflow guidance: run fixes for anything with ok=false and optional=false, rerun the doctor, and the install becomes operational. It also warns that credential checks are optional and require human involvement. It does not explicitly compare against sibling tools, but the usage context is clear enough that an agent would know when to invoke it.
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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