Historical market memory for AI agents using semantic vector search across years of financial market data. Discover similar market regimes, price patterns, and market context for quantitative research and algorithmic trading.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 3.6/5 across 12 of 12 tools scored. Lowest: 2.8/5.
There is notable overlap between pattern_search, find_market_analogs, and search_by_sketch, all of which search historical price patterns with slightly different inputs and outputs. Similarly, get_trading_decision and get_live_polymarket_trade_decision both return trade recommendations, though one is specific to Polymarket. However, the descriptions do provide enough detail to distinguish them in most cases.
Tool names mostly follow a verb_noun snake_case pattern (e.g., backtest_strategy, detect_market_regime, get_pattern_metrics). The main deviation is pattern_search, which is noun_verb, and the inconsistent use of prefixes (get_ vs. other verbs). Overall, the naming is predictable and readable.
With 12 tools, the server is well-scoped for a price pattern analysis and trading decision domain. Each tool serves a distinct purpose, and the count falls squarely in the ideal 3-15 range. The inclusion of meta tools like get_api_guide and get_mcp_compatibility_manifest is reasonable for a developer-facing server.
The tool surface covers the core workflow: pattern search, analog analysis, regime detection, backtesting, trading decisions, and performance tracking. Minor gaps exist, such as no explicit tool for fetching raw market data or managing pattern results, but these can be worked around and are not essential to the stated purpose.
backtest_strategyAInspectPerform a full strategy backtest over a historical period (Walk-forward analysis). Use this for testing general rules or long-term performance.
| Name | Required | Description | Default |
|---|---|---|---|
| f | Yes | Forecast horizon | |
| q | Yes | Query length | |
| step | No | Simulation step in bars | |
| topK | No | Number of nearest neighbours | |
| endTs | No | End timestamp for simulation | |
| feePct | No | Per-side fee percentage | |
| symbol | Yes | Ticker symbol | |
| maxBars | No | Maximum recent bars to load for the backtest | |
| minProb | No | Directional probability threshold | |
| startTs | No | Start timestamp for simulation | |
| interval | Yes | Timeframe | |
| token_id | No | Optional Manus access token. Paid tools use tokenized service access, not a monthly subscription: when token_id is omitted the server returns payment_required with a Solana Pay invoice, and after payment you retry with the same token while the server uses Manus token/resolve to recover pending access. | |
| direction | No | Allowed direction: long, short, or both | |
| minAvgSim | No | Minimum average similarity required to trade | |
| onlySignals | No | Return only non-neutral decisions | |
| slippagePct | No | Per-side slippage percentage | |
| includeStats | No | ||
| embeddingMode | No | Pattern embedding mode for ANN retrieval |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description should disclose behavioral traits like side effects, data mutation, permissions, or computational costs. It only describes the action and purpose, missing any transparency about what happens during the backtest or whether it produces side effects. The lack of disclosure is significant for a complex tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences: the first states the core action and method, the second provides usage guidance. It is front-loaded with the essential purpose and contains no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This tool has 18 parameters and no output schema or annotations, yet the description gives only a high-level purpose. It does not explain walk-forward analysis, parameter interactions, or return values, leaving the agent under-informed for a complex operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is high (94%), so the input schema already documents parameter meanings thoroughly. The description adds no additional parameter insights, but the baseline of 3 applies because the schema carries the burden.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Perform') and names a concrete resource ('full strategy backtest') with a defined methodology ('Walk-forward analysis'). It clearly distinguishes this from siblings by focusing on historical backtesting for general rules and long-term performance, rather than single decisions or pattern searches.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
'Use this for testing general rules or long-term performance' provides clear context for when to invoke this tool. However, it does not explicitly state when not to use it or mention alternatives, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
detect_market_regimeCInspectClassify current market state into one of the known market regimes.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | Ticker symbol | |
| interval | Yes | Timeframe | |
| token_id | No | Optional Manus access token. Paid tools use tokenized service access, not a monthly subscription: when token_id is omitted the server returns payment_required with a Solana Pay invoice, and after payment you retry with the same token while the server uses Manus token/resolve to recover pending access. | |
| timestamp | No | Unix timestamp for detection | |
| queryLength | Yes | Length of price vector to analyze |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden for behavioral disclosure. It only states the tool's classification purpose and gives no details about read-only nature, required historical data, potential side effects, or edge cases. The absence of any behavioral caveats is a notable gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no wasted words. It immediately communicates the core function and is appropriately sized for a straightforward classification task.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having a complete input schema, the description omits any information about return values, output format, or required market data length. With no output schema and no behavioral context, the description is insufficient for an agent to fully understand what the tool produces or what conditions affect its results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers all five parameters with descriptions, achieving 100% schema_description_coverage. The tool description adds no extra parameter guidance, but the schema sufficiently documents the inputs, 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('classify') and resource ('market state into known market regimes'), clearly stating what the tool does. It is distinguishable from sibling tools by its focus on regime detection, though it doesn't explicitly name alternative tools for differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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. There is no mention of prerequisites, ideal scenarios, or exclusions, leaving the agent without context for tool selection or invocation timing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
find_market_analogsAInspectFind historical price patterns similar to the current (or specified) market state. Returns a list of past dates when the same pattern occurred, the price outcome after each analog, and aggregate statistics (win rate, median return, percentile range). Use cases: (1) pre-news analysis — filter by timeOfDayUTC to find analogs that happened near a specific event time (e.g., FOMC at 14:00 UTC); (2) regime research — understand historically what happens after this pattern; (3) Polymarket context — combine with get_polymarket_probabilities to validate signal with historical evidence. Returns a plain-English summary suitable for agent reasoning.
| Name | Required | Description | Default |
|---|---|---|---|
| f | No | ||
| q | No | ||
| limit | No | ||
| symbol | Yes | Ticker symbol, e.g. BTCUSDT | |
| context | No | ||
| session | No | ||
| interval | No | Candle interval | 15m |
| token_id | No | Optional Manus access token. Paid tools use tokenized service access, not a monthly subscription: when token_id is omitted the server returns payment_required with a Solana Pay invoice, and after payment you retry with the same token while the server uses Manus token/resolve to recover pending access. | |
| weekdays | No | ||
| timeOfDayUTC | No | ||
| timeRangeUTC | No | ||
| minSimilarity | No | ||
| anchorTimestamp | No | ||
| timeWindowMinutes | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses return structure (list of dates, outcomes, aggregate stats, plain-English summary) and mentions a specific filtering behavior (timeOfDayUTC). It does not discuss error/payment behavior or edge cases, but adds meaningful 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded: purpose in the first sentence, return details next, then enumerated use cases. It is neither terse nor wordy, and every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the high complexity (14 params, nested objects, no output schema), the description provides useful high-level context and use cases but does not sufficiently explain the parameter space or how to use more advanced filters. It is adequate for basic use but incomplete for full utilization.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 21%, and the description only meaningfully explains timeOfDayUTC among the many parameters. Parameters like f, q, minSimilarity, timeRangeUTC, weekdays, and anchorTimestamp are not explained in the description, leaving significant gaps in understanding how to configure a good query.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool finds historical price patterns similar to the current or specified market state and lists the concrete outputs (past dates, outcomes, aggregate statistics). This specific verb+resource definition distinguishes it from sibling tools like backtest_strategy or detect_market_regime.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives three explicit use cases: pre-news analysis (with timeOfDayUTC filtering), regime research, and combining with get_polymarket_probabilities. It lacks explicit 'do not use when...' exclusions or direct comparison with alternative tools, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
forecast_private_memory_from_dataBInspectCreate or reuse a private dataset, import candle data, and return a compact forecast card in one call. Ideal for users who want to upload exchange or ML-derived candle history, inspect analogues immediately, and only then decide whether to run RLXBT.
| Name | Required | Description | Default |
|---|---|---|---|
| f | No | ||
| q | No | ||
| name | No | ||
| rows | No | ||
| slug | No | ||
| sort | No | ||
| force | No | ||
| limit | No | ||
| start | No | ||
| cursor | No | ||
| userId | No | ||
| agentId | No | ||
| rawText | No | ||
| anchorTs | No | ||
| interval | No | ||
| token_id | No | Optional Manus access token. Paid tools use tokenized service access, not a monthly subscription: when token_id is omitted the server returns payment_required with a Solana Pay invoice, and after payment you retry with the same token while the server uses Manus token/resolve to recover pending access. | |
| datasetId | No | ||
| description | No | ||
| windowEndTs | No | ||
| embeddingMode | No | ||
| windowStartTs | No | ||
| includeForecast | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose side effects and behavioral traits. It mentions 'create or reuse a private dataset,' indicating a potential write operation, and 'return a compact forecast card,' but it does not clarify persistence, cost, or other side effects. The token_id parameter in the schema describes payment, but the description itself omits this, leaving the agent under-informed about behavioral consequences.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact—two sentences with no filler. It front-loads the main action and then adds the use case. While it could be longer given the tool's complexity, the brevity is not wasteful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 22-parameter tool with no output schema and no annotations, this two-sentence description is severely incomplete. It lacks guidance on inputs, output structure, and edge cases like tokenized payment. It covers the high-level purpose but leaves the agent with many unanswered questions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 5%, so the description should compensate by explaining parameter semantics. It vaguely alludes to 'candle data' and 'dataset' but does not map to specific fields like rows, datasetId, or interval. No direction is given on which parameters are needed for creation vs. reuse.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Create or reuse a private dataset, import candle data, and return a compact forecast card in one call.' It identifies a specific multi-step workflow and even names the intended use case. However, it does not explicitly contrast with sibling tools like find_market_analogs, so it earns a 4 rather than a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear usage context: 'Ideal for users who want to upload exchange or ML-derived candle history, inspect analogues immediately, and only then decide whether to run RLXBT.' This tells when to use the tool (combined upload-inspect-forecast workflow). It does not explicitly state exclusions or name alternatives, so a 4 is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_api_guideAInspectReturns the full documentation and workflow guide for this MCP server. Call this first to understand all available tools, their use cases, and how to combine them. Includes: tool catalog, recommended workflows for Polymarket research, historical analog analysis, and example calls.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | No | Focus area: 'all' for full guide, or a specific topic | all |
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 clearly states that the tool returns documentation and includes a catalog, workflows, and examples, implying read-only behavior. It does not claim any side effects or mutations, and its purpose is inherently non-destructive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, using three sentences that front-load the main purpose ('Returns the full documentation...'), followed by usage guidance and content highlights. Every sentence adds value with no extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that the tool is a meta-documentation guide with a single optional parameter and no output schema, the description covers the essential context: what it returns, what it includes, and when to call it. It could mention the output format (e.g., plain text vs. structured), but that is a minor omission.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for the 'topic' parameter, with enum values and a clear description. The description adds no additional parameter semantics beyond what the schema already provides, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Returns the full documentation and workflow guide for this MCP server.' This uses a specific verb ('Returns') and resource, and distinguishes it from sibling tools that perform specific tasks like backtesting or pattern search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly instructs 'Call this first to understand all available tools, their use cases, and how to combine them.' This provides clear when-to-use guidance and establishes it as the entry point, eliminating confusion with other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_live_polymarket_trade_decisionBInspectAuto-discover the active BTC Up/Down 15m Polymarket market, combine live Polymarket pricing with pattern memory and intrabar momentum, and return BUY_YES, BUY_NO, or SKIP with entry guardrails.
| Name | Required | Description | Default |
|---|---|---|---|
| nowMs | No | ||
| symbol | No | BTCUSDT | |
| minEdge | No | ||
| interval | No | 15m | |
| token_id | No | Optional Manus access token. Paid tools use tokenized service access, not a monthly subscription: when token_id is omitted the server returns payment_required with a Solana Pay invoice, and after payment you retry with the same token while the server uses Manus token/resolve to recover pending access. | |
| marketWindow | No | current | |
| allowDegradedSearch | No | ||
| minCombinedConfidence | No |
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 states that the tool returns a decision (BUY_YES/BUY_NO/SKIP) with entry guardrails, but it does not disclose whether trades are executed, side effects (e.g., network calls, payment requirements), or what happens when payment is needed. The description omits important behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, dense sentence that packs the main actions without repetition or filler. It is front-loaded with the primary verb and resource, but slightly overloaded with multiple clauses. Still efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 8 parameters, no output schema, and no annotations, yet the description only provides a high-level overview. It does not explain the return format, what 'entry guardrails' are, the payment flow hinted at by token_id, or failure modes. For a complex tool of this type, the description is insufficient for an agent to invoke it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 13%, so the description must compensate. It does not explain key parameters like minEdge, allowDegradedSearch, minCombinedConfidence, or marketWindow. While symbol and interval are implicit in the market description, most parameters remain under-documented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses specific verbs ('auto-discover', 'combine', 'return') and names a specific resource ('active BTC Up/Down 15m Polymarket market'). It clearly distinguishes itself from sibling tools like get_trading_decision by focusing on the live Polymarket market and entry guardrails.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The context is implied: this is for live trading decisions on the specific Polymarket BTC Up/Down 15m market. However, there is no explicit 'when to use' or 'when not to use' guidance, nor any mention of alternative tools like get_trading_decision or backtest_strategy.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_mcp_compatibility_manifestAInspectReturn the versioned MCP compatibility manifest, including canonical tools, aliases, and JSON argument schemas for remote clients.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It indicates a read-only 'Return' operation and notes the versioned nature, but does not mention authentication, rate limits, or potential side effects. The description adds some value beyond the name but lacks full transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that front-loads the core action ('Return the versioned MCP compatibility manifest') and then details contents. Every word earns its place, with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple, parameterless retrieval tool, the description is largely complete. It specifies the manifest contents and target audience (remote clients). However, it does not detail the output format or how versioning is handled, leaving minor gaps for a fully self-contained understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema is empty. Per the baseline for 0-parameter tools, a score of 4 is appropriate. The description does not need to explain parameters and instead clarifies the return content, adding semantic value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns a versioned MCP compatibility manifest with specific contents (canonical tools, aliases, JSON schemas). This distinguishes it from siblings like get_api_guide or pattern_search, making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for remote clients needing machine-readable tool schemas, but it does not explicitly state when to use this tool versus alternatives or provide exclusions. The lack of explicit guidance lowers the score, though the purpose is clear enough to infer appropriate use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_pattern_metricsAInspectRetrieve advanced statistical metrics and forecast distributions for a pattern search result.
| Name | Required | Description | Default |
|---|---|---|---|
| f | No | Forecast horizon in bars | |
| q | No | Query length in bars | |
| sort | No | Sort mode for results | |
| limit | No | Maximum number of matches to return | |
| symbol | No | Ticker symbol (e.g., BTCUSDT) | |
| compact | No | Default true: strips per-match value arrays and full forecast paths, returning match metadata, horizon-end price targets, and the calibrated outcome distribution (~4x fewer tokens). Set false for full arrays. | |
| anchorTs | No | Target timestamp to search around | |
| interval | No | Timeframe (e.g., 1h, 15m) | |
| token_id | No | Optional Manus access token. Paid tools use tokenized service access, not a monthly subscription: when token_id is omitted the server returns payment_required with a Solana Pay invoice, and after payment you retry with the same token while the server uses Manus token/resolve to recover pending access. | |
| embeddingMode | No | Pattern embedding mode: priceShape uses normalized closes, featureV1 uses OHLCV/context, neuralV1 uses the experimental ONNX encoder |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description states 'Retrieve' which correctly indicates a read-only operation. With no annotations provided, the description carries the full burden, but it does not disclose the token_id payment behavior or other side effects (though these are in the schema). This is adequate but not rich for a tool with a significant payment-related parameter.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no unnecessary words. It efficiently conveys the core purpose without rambling.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite the rich schema, the description is short for a tool with 10 parameters and no output schema. It doesn't describe the return format or the relationship to pattern_search output, but the schema and sibling names provide substantial context, making the description minimally viable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers all 10 parameters with descriptions (100% coverage), so the baseline score is 3. The description adds no parameter-level meaning beyond the schema, so it neither improves nor degrades the score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Retrieve' and identifies the resource as 'advanced statistical metrics and forecast distributions' for a pattern search result. This clearly distinguishes it from sibling tools like pattern_search, which likely performs the actual search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'for a pattern search result' implies the tool should be used after a search, providing clear context for when to use it. However, it does not explicitly name alternative tools or state when not to use it, missing the 'when/not/alternatives' bar for a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_track_recordAInspectPublic forecast track record: every closed bar the engine logs its own forecast BEFORE the outcome is known, then grades it against reality. Returns direction hit rate, p10-p90 band coverage vs the nominal 80%, mean abs error, the active conformal calibration scale, and recent forecast-vs-actual rows. Use this to verify the engine's calibration before trusting its forecasts.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max recent forecast rows to return (default 40, max 200) | |
| symbol | No | Ticker symbol (default BTCUSDT) | |
| interval | No | Timeframe: 5m, 15m, 1h or 4h (default 1h) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full transparency burden. It discloses the methodology (logs forecasts before outcome, grades against reality), the public nature, and the specific metrics returned. This goes well beyond a simple 'get' but doesn't address potential permissions or side effects; however, the tool appears inherently read-only.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loaded with the core concept, followed by a concise list of returned data, and ends with practical usage guidance. Every sentence earns its place with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description adequately enumerates the key return fields (hit rate, band coverage, MAE, calibration scale, recent rows). It also explains the timing and public nature. While it doesn't detail formatting or edge cases, it is complete enough for an agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with each parameter documented including defaults. The description adds context about outputs but no additional parameter-level meaning. This meets the baseline of 3 for well-covered schemas.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: accessing a public forecast track record. It specifies the resource ('public forecast track record') and the action (returns quality metrics), distinguishing it from siblings like backtest_strategy or get_trading_decision by focusing on calibration verification.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says when to use it: 'Use this to verify the engine's calibration before trusting its forecasts.' This provides clear context, though it stops short of naming alternatives or explicit when-not-to-use scenarios, which would merit a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_trading_decisionBInspectReturn a compact trader decision card for a symbol/interval: TRADEABLE, WATCH, or SKIP with direction, confidence, evidence, risk, reasons, and caveats.
| Name | Required | Description | Default |
|---|---|---|---|
| f | No | Forecast horizon in bars | |
| q | No | Query length in bars | |
| sort | No | Sort mode for results | |
| limit | No | Maximum number of matches to return | |
| feePct | No | Per-side fee percentage for the proof backtest | |
| symbol | No | Ticker symbol (e.g., BTCUSDT) | |
| compact | No | Default true: strips per-match value arrays and full forecast paths, returning match metadata, horizon-end price targets, and the calibrated outcome distribution (~4x fewer tokens). Set false for full arrays. | |
| minProb | No | Minimum directional probability required to trade in the proof backtest | |
| anchorTs | No | Target timestamp to search around | |
| interval | No | Timeframe (e.g., 1h, 15m) | |
| token_id | No | Optional Manus access token. Paid tools use tokenized service access, not a monthly subscription: when token_id is omitted the server returns payment_required with a Solana Pay invoice, and after payment you retry with the same token while the server uses Manus token/resolve to recover pending access. | |
| minAvgSim | No | Minimum average similarity required to trade in the proof backtest | |
| slippagePct | No | Per-side slippage percentage for the proof backtest | |
| backtestStep | No | Backtest step in bars | |
| backtestEndTs | No | Optional backtest end timestamp in Unix ms | |
| embeddingMode | No | Pattern embedding mode: priceShape uses normalized closes, featureV1 uses OHLCV/context, neuralV1 uses the experimental ONNX encoder | |
| backtestMaxBars | No | Maximum recent bars to load for the backtest | |
| backtestStartTs | No | Optional backtest start timestamp in Unix ms | |
| includeBacktest | No | When true, include a walk-forward backtest proof block. This is slower than the default decision-only response. | |
| backtestTimeoutMs | No | Optional timeout budget for the backtest request |
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 only describes the output card contents, but does not disclose that the tool may require a paid token, may return a payment_required response, or that it can optionally run a backtest (as implied by includeBacktest). It also doesn't mention performance characteristics or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, front-loaded with the key action ('Return a compact trader decision card') and then lists the output components concisely. Every word adds value; there is no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 20 parameters, no annotations, and no output schema, the description is incomplete. It explains the decision card output but omits the backtest proof block, the token payment mechanism, and the distinction between compact and full output modes. It does not provide enough context for an agent to know about important behaviors like includeBacktest or the need for token_id.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the baseline is 3. The description does not add parameter-level meaning beyond mentioning 'symbol/interval', which is already in the schema. It does not explain differences between parameters like f, q, embeddingMode, or backtest options.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns a 'compact trader decision card' with specific possible outcomes (TRADEABLE, WATCH, SKIP) and listed content fields (direction, confidence, evidence, risk, reasons, caveats). This is a specific verb+resource construction that distinguishes it from siblings like backtest_strategy or detect_market_regime.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 alternatives. With a sibling tool like get_live_polymarket_trade_decision, the description does not clarify the difference or mention use cases. There is no context for when to prefer this over backtest_strategy or find_market_analogs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
pattern_searchCInspectSearch for similar historical price patterns in the RLX database.
| Name | Required | Description | Default |
|---|---|---|---|
| f | No | Forecast horizon in bars | |
| q | No | Query length in bars | |
| sort | No | Sort mode for results | |
| limit | No | Maximum number of matches to return | |
| symbol | No | Ticker symbol (e.g., BTCUSDT) | |
| compact | No | Default true: strips per-match value arrays and full forecast paths, returning match metadata, horizon-end price targets, and the calibrated outcome distribution (~4x fewer tokens). Set false for full arrays. | |
| anchorTs | No | Target timestamp to search around | |
| interval | No | Timeframe (e.g., 1h, 15m) | |
| token_id | No | Optional Manus access token. Paid tools use tokenized service access, not a monthly subscription: when token_id is omitted the server returns payment_required with a Solana Pay invoice, and after payment you retry with the same token while the server uses Manus token/resolve to recover pending access. | |
| embeddingMode | No | Pattern embedding mode: priceShape uses normalized closes, featureV1 uses OHLCV/context, neuralV1 uses the experimental ONNX encoder |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden of behavioral disclosure. It only states the action without revealing output format, pagination, rate limits, authentication needs, or whether the operation is read-only. It implies a read/search operation but does not explicitly confirm safety or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no wasted words. However, it is under-specified for a tool with 10 parameters and no other context, making it too terse to be considered well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description must provide context about what the tool returns and how it behaves. It does neither. A complex tool like this needs an overview of use cases, result format, and any special considerations, all of which are missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All 10 parameters have detailed descriptions in the input schema (100% coverage), so the description need not add parameter details. The description itself adds no additional semantic meaning beyond the schema, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Search') and resource ('similar historical price patterns in the RLX database'), making the core purpose clear. However, it does not differentiate from the sibling tool 'find_market_analogs', which appears to serve a similar function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 over alternatives such as 'find_market_analogs' or 'search_by_sketch'. There are no use cases, prerequisites, or exclusions mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_by_sketchBInspectSearch for historical patterns similar to a custom 'sketched' price trajectory (Sketch-to-Search). Useful when you want to find matches for a hypothetical or hand-drawn pattern.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max matches to return | |
| symbol | No | Reference symbol for scale | BTCUSDT |
| interval | No | Reference interval | 1h |
| token_id | No | Optional Manus access token. Paid tools use tokenized service access, not a monthly subscription: when token_id is omitted the server returns payment_required with a Solana Pay invoice, and after payment you retry with the same token while the server uses Manus token/resolve to recover pending access. | |
| queryValues | Yes | Array of price points representing the sketched pattern (e.g. [10, 11, 10.5, 12]) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It only says 'search,' implying read-only, but omits significant behavioral traits such as the token_id payment requirement (server returns payment_required if omitted), rate limits, or result format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief and front-loaded with the main action. It wastes no words, though the parenthetical '(Sketch-to-Search)' could be considered slightly unnecessary.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description is too short to provide complete context. It fails to mention token/payment behavior, return details, or operational prerequisites, making it insufficient for a tool with this many parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already documents all parameters. The description adds minimal semantic value by referring to the sketched pattern, but doesn't elaborate on parameter syntax or relationships beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('search for historical patterns') and resource ('custom sketched price trajectory'), distinguishing it from siblings like pattern_search by the input method. However, it does not explicitly name alternatives or contrast behaviors.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives a clear when-to-use scenario ('useful when you want to find matches for a hypothetical or hand-drawn pattern'), but lacks explicit exclusions or direct comparison to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!
