NythosX Market Data

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

With no annotations, the description carries the full burden and does so well: one-minute polling cadence, single-fire behavior, HMAC-SHA256 signing with the exact header format and key source, TTL defaults/bounds, and a per-call cost with payment protocol. This is far beyond what a bare name implies.

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?

Purpose is front-loaded in the first clause, followed by tightly packed operational details. The signing sentence is dense but each clause carries non-redundant information; nothing is wasted.

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?

No output schema and no annotations, yet the description covers firing behavior, signing, TTL, cost, and status-check path. It stops short of describing the event payload shape or webhook retry/failure behavior, which an agent might want.

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 low (40%), so the description must compensate, and it adds the ttl_hours default (168) and the webhook_url signing contract. price/direction/symbol semantics remain unelaborated, though they are largely self-evident from the enum and type.

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?

States a specific verb and resource ('Arm a price trigger with webhook delivery') plus the mechanism (polling + signed POST). It also distinguishes itself from the sibling get_trigger_status by noting status checks are free at a separate endpoint.

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?

Gives clear operational context: single-fire semantics, first-crossing-only firing, and where to check status instead of re-arming. It does not explicitly state when arming is inappropriate or contrast with any other arming alternative.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full behavioral burden. It usefully discloses the refresh cadence (every 15 minutes) and that the tool is free, but omits permissions, rate limits, and whether the data is cached or live.

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 sentence that front-loads the returned data and appends key context (refresh interval and free tier). Every element earns its place with no 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 read-only snapshot tool with no output schema, the description names the data returned but does not describe the response structure or explain how to choose it over the more granular siblings. It is minimally complete but leaves clear gaps.

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 single parameter has 0% schema description coverage, but its enum already lists BTC and ETH. The description repeats those same symbols without adding meaning beyond the enum, so it only partially compensates for the lack of textual schema description.

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 verb (implied by 'get'), resource (market regime and nearest support/resistance), and scope (BTC and ETH). It does not explicitly distinguish itself from the separate get_market_regime and get_support_resistance siblings, but it implies a combined snapshot.

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 given on when to use this snapshot versus the specialized sibling tools like get_market_regime or get_support_resistance. The only contextual cue is 'refreshed every 15 minutes (free)', which hints at freshness and cost but does not route the agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full burden, and it does disclose meaningful operational facts: data granularity (hourly candles) and a paid invocation model ($0.005 USDC per call via x402), which tells the agent this call has a real cost and requires payment handling. It does not cover rate limits, error behavior, or data freshness, so the disclosure is strong but not complete.

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?

Two sentences, front-loaded with the purpose and then the exact computed signals, with the cost caveat last. Every clause carries information; nothing is redundant padding.

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 compensates well by enumerating the indicators returned (trend, volatility bucket, RSI, EMAs) and noting granularity and cost. It falls short only in omitting usage context relative to sibling analysis tools and any freshness/caching notes.

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?

There is one parameter (symbol) at 100% schema description coverage, with a full 13-value enum in the schema. The description adds nothing about the symbol parameter, so the schema does all the work; baseline 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 names the resource (market regime) and enumerates exactly what is computed: trend up/down/range via EMA50 vs EMA200, volatility bucket via ATR%, RSI, and EMAs, all from hourly candles. That level of specificity clearly separates it from siblings like get_support_resistance or get_target_probability, though no sibling is named explicitly.

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?

There is no guidance on when to call this tool versus the other market-analysis siblings (get_support_resistance, get_advisor_verdict, get_target_probability). The description is purely descriptive of output content and never states a use case or exclusion.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full disclosure burden and does well: it states the output is identical for every caller (deterministic, non-personalized), that accuracy is auditable at /api/v1/track-record, and that the call costs $0.01 USDC via x402 — an important payment/auth prerequisite an agent must know before invoking. Rate limits and error behavior remain unstated.

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?

Front-loaded with the core purpose, followed by a dense but scannable inventory of outputs. Two sentences, both earning their place, though the second sentence is heavy with enumerations.

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?

There is no output schema, so the description must describe return values — and it does thoroughly (bias label set, score, confidence, entry/stop/target/risk-reward, factor list, invalidation conditions). Combined with the pricing and track-record disclosure, an agent has everything needed to call and interpret this tool.

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% and both parameters (symbol, tf) have enum values plus a default documented in the schema, so the description need not restate them. It adds no parameter-level syntax or constraint detail beyond the schema, making the baseline 3 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 names a specific resource (signal bias) and enumerates exactly what it produces: bias classification, score, confidence, setup levels, weighted factors, and invalidation conditions. It is clearly distinguishable from siblings like get_trade_plan or get_market_regime by its output composition, though it never explicitly contrasts itself with them.

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 when-to-use guidance is given. It mentions get_track_record's track record page only to vouch for accuracy, not as a routing alternative, and nothing tells the agent when get_signal_bias should be chosen over get_trade_plan or get_market_regime.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full burden: it discloses the pricing model ($0.005 USDC per call via x402), the output volume cap (five levels per side), the derivation methods (clustered daily swing pivots, high-volume nodes, round numbers), and the per-level fields including strength scale and touch count. It omits caching, latency, or failure behavior, but the payment and derivation context is unusually informative.

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?

Three short sentences ordered as what-it-is, what-it-returns, what-it-costs; every clause carries information and nothing is redundant.

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?

No output schema exists, so the description takes on the return-value burden and does so thoroughly by listing the level fields and the nearest-level-each-side addition. The main shortfall is that it never explains how a caller should act on the result or the resolution/timeframe of the levels, but it is sufficient to invoke 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?

Only one parameter exists and schema description coverage is 100%, with the enum enumerating every supported instrument, so the schema does the heavy lifting. The description adds no syntax, timeframe, or formatting meaning beyond what the schema already provides, which lands at the baseline 3.

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 names a specific resource (support and resistance levels) and details exactly what the output contains, so an agent knows this computes technical levels rather than a regime or probability verdict. It does not explicitly contrast itself with siblings like get_market_regime or get_target_probability, which keeps it at 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?

There is no statement of when to reach for this tool versus the other analysis tools in the family, and no prerequisites or exclusions are given. Usage is only implied by the technical-analysis nature of the resource.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full burden, and it does disclose meaningful traits: a per-call cost of $0.005 USDC via x402 (a real invocation constraint) and the underlying method (14-day ATR normal approximation, touch ≈ 2x close-beyond). It does not describe error handling, symbol coverage limits, or response shape.

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?

Front-loaded with the core purpose, and the methodology and cost details are compressed into a parenthetical. The opening two sentences partially restate each other ('probability of reaching a target price' vs 'probability that the price closes beyond a target'), which is minor 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?

No output schema exists, so the description must convey returns, and it does: close-beyond probabilities at three horizons plus a touch probability. Combined with the cost disclosure, an agent has enough to call and interpret it, though nothing is said about invalid symbols or failure modes.

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% for both parameters, so the schema already defines symbol and target. The description adds only implicit context that target is evaluated against closes at 1/7/30-day horizons, which is the baseline level of added value when the schema does the heavy lifting.

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 precise computation: the probability that price closes beyond a target at 1, 7 and 30 days, plus a touch probability. It is clearly distinct from siblings like get_support_resistance or get_market_regime, though it never explicitly contrasts itself with them.

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?

There is no statement of when to use this tool versus alternatives such as get_support_resistance or get_advisor_verdict, and no prerequisites or exclusions. The only guidance-like content is the per-call cost.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full burden. It discloses the returned metrics (hit rate, Wilson intervals, Brier score, independent samples) and that access is free, but says nothing about auth requirements, rate limits, or whether results are cached. Adequate but not rich.

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?

One dense sentence, front-loaded with what is returned and the free-access note. The colon-separated metric list is efficient, though the punctuation reads slightly awkwardly.

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?

No output schema exists, so listing the metrics is genuinely useful. However, with zero required parameters, the description never says what happens when symbol is omitted (all-symbol aggregate vs. error), which is the one behavior an agent must know before calling.

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% with an enum for symbol, so the schema already documents the parameter fully; baseline 3 applies. The phrase 'by symbol' adds only a faint hint that the symbol scopes the breakdown rather than being required.

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?

States a specific resource and scope: graded accuracy metrics for every published signal bias. It is clearly distinguishable from get_signal_bias (current bias) and get_target_probability, though it never names a sibling to sharpen the boundary.

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 when-to-use, when-not-to-use, or alternative tool is mentioned. The '(free)' tag hints at a tiering distinction but the agent is left to infer that this is the historical-performance counterpart to the other signal tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full behavioral burden and does well: it discloses the paid nature and price ($0.03 USDC per call via x402), enumerates the return payload, and specifies the special neutral-bias semantics. It does not cover failure modes, latency, or what happens for unsupported symbol/timeframe combos, so it is not exhaustive.

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?

It is front-loaded with the value proposition and then lists outputs efficiently in one dense sentence. There is mild redundancy between 'Complete trade setup in one call' and 'One call instead of four', keeping it just short of a 5.

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?

There is no output schema, so the description must describe the return payload — and it does so field by field, plus it surfaces the pricing model and the neutral-bias branch. For a two-parameter aggregation tool, an agent has everything needed to decide to call it and interpret the result.

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% and both parameters (symbol, tf) have descriptions plus enums, so the schema already does the heavy lifting. The description adds no syntax or meaning beyond what the schema provides, which is the correct baseline-3 case.

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 names a concrete verb+resource ('Complete trade setup') and enumerates exactly what the setup contains (bias, regime, S/R, entry/stop/target/R:R, probabilities, invalidation, track record). It also implicitly distinguishes itself from siblings by framing the tool as 'One call instead of four' while the sibling list contains get_signal_bias, get_market_regime, get_support_resistance, get_target_probability, and get_track_record individually.

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 'One call instead of four' framing tells the agent when to prefer this aggregate over the individual sibling tools, and the neutral-bias note clarifies an edge-case behavior. There is no explicit when-not guidance (e.g., cost-sensitive scenarios or when only one component is needed), so it stops 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.

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

With no annotations, the description carries the full burden of behavioral disclosure. It usefully notes that the tool is free and tied to triggers created by arm_price_trigger, but it does not confirm read-only behavior, describe error cases, or explain what the status response contains.

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 sentence that front-loads the resource and scope with zero wasted words. It is appropriately sized for a simple status lookup tool.

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 one-parameter read tool with no output schema, the description gives the essential scope and cost. It falls short by not describing the return value at all, which is important because no output schema exists to document it.

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 has 0% description coverage for the single required 'id' parameter, so the description must compensate. It implies the id refers to a trigger armed with arm_price_trigger, but it never explicitly mentions the parameter, its expected format, or how to obtain the id.

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 the resource (a trigger) and the scope (armed with arm_price_trigger), and names the sibling tool that creates it. It is clear what the tool retrieves, though it does not spell out what fields the status includes.

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 'armed with arm_price_trigger' implies the tool is used after calling arm_price_trigger, giving implicit context. However, there is no explicit when-to-use guidance, no exclusions, and no mention of alternative tools for other trigger types.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full burden. It usefully discloses the return contents and notes the call is '(free)', implying no credit or cost is incurred, which is genuine behavioral context. It does not state that it takes no arguments or that the catalog is static, so it stops short of 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?

A single front-loaded sentence that lists the payload without filler. Every clause carries information and nothing is padded.

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, the description must convey what comes back, and it does so by naming the four returned data categories. The phrase 'tool prices' is slightly ambiguous (cost per tool call versus asset prices), which is the one small gap for a zero-argument, annotation-free listing tool.

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 takes zero parameters, so there is nothing for the description to disambiguate and the baseline of 4 applies. The schema confirms an empty object, so no additional parameter detail is required or missing.

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 gives a specific verb (List) and enumerates the resources returned: supported symbols, timeframes, tool prices, and the payment network. That is far more informative than a tautology, but it does not distinguish itself from the sibling tools or explain why a trading agent would call this discovery endpoint rather than one of the analysis tools.

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?

There is no explicit when-to-use guidance and no mention of alternatives among siblings such as get_market_regime or get_support_resistance. The only implied usage is that it is a lookup/discovery call, which the agent must infer on its own.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.