mcp.xynaptic

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 pricing model ([$0.020 per call, x402/USDC]) and the registry-filtering behavior ('active, officially registered'), but says nothing about return format, result limits, or pagination.

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 core purpose and scoping. 'Xynaptic' branding and the slight redundancy between 'officially registered' and 'official registry only' are minor wastes, but the size is appropriate.

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 two-parameter search with no output schema, the description covers purpose, cost, and registry filtering, which is enough to invoke correctly. It stops short of describing returned fields or result volume, leaving a modest gap.

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 nested trade/dept fields and their examples are already documented. The description adds trade examples (plombier, electricien, menuisier) but no new syntax or constraints, so baseline 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?

States a specific verb and resource: 'find active, officially registered French tradespeople by trade and department.' It also pins the scope ('official registry only'), so an agent can tell exactly what this returns versus generic search siblings like company-search or tender-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 'Usage:' sentence indicates when to use it (finding French tradespeople by trade/department), but it mostly restates the purpose rather than giving conditions or alternatives. No when-not guidance or sibling routing is provided, though no close sibling exists.

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 cost ($0.020 per call, x402/USDC) and the accepted input formats (JSON transactions array or CSV). It also implies a stateless compute operation but does not detail output structure, error handling, or idempotency—minor gaps for a simple categorization 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 front-loaded with the core purpose and usage, then alternatives and cost. It is efficient overall, though the opening restates the tool name and the bracketed price at the end feels tacked on, slightly breaking flow.

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 no annotations, no output schema, and nested input, the description covers purpose, usage constraints, alternatives, pricing, and input formats. It stops short of describing the exact return shape or failure modes, but those are less critical for this simple categorization 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 description coverage is 100%, so the schema already documents both parameters. The description clarifies the main payload shape (transactions array or CSV alternative), which adds marginal value, but it does not explain the 'output' or 'params' fields—leaving the structured schema to do the heavy lifting as expected at 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 states a specific verb (categorize) and resource (raw bank transactions), and specifies the outcome: standard categories with merchant and income/expense detection. It also distinguishes itself from deeper analytics tools by pointing to xynaptic-catalog, so an agent can tell what it is and is not for.

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?

It explicitly says 'Use ONLY for raw unlabelled transactions. If they are already categorized, skip this,' giving both a when-to-use and a when-not-to-use condition. It also names the alternative (deeper analytics via xynaptic-catalog), leaving no ambiguity about routing.

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?

No annotations are provided, so the description carries the full burden. It usefully discloses the costing/payment model ('$0.020 per call, x402/USDC') and that the data is live futures, but omits auth requirements, rate limits, and error behavior expected of a paid live endpoint.

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 resource and asset list, but the prose is comma-heavy and repeats itself ('live commodity prices' then 'Live futures data') while the pricing/payment note trails at the end rather than up front.

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 and no annotations, so the description should explain more. It covers scope, example calls, and cost, but leaves the return shape ('daily change') and the odd schema params (price/currency/change_pct) unaddressed, leaving the agent to guess at the actual contract.

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%, so the baseline is 3. The description adds the query-string syntax '?asset=copper' and a batched '?assets=copper,gold' form, but the schema only defines a singular 'asset' plus response-like fields (price, currency, change_pct), so the added batch hint is only loosely reconciled with the actual input contract.

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+resource ('live commodity prices') and enumerates the covered assets (Brent, WTI, gas, copper, gold, lithium, wheat, etc.), which clearly separates it from crypto-price, security-price, and property-price. An agent can identify the domain without opening the schema.

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?

Provides an explicit 'Usage: Use for live commodity spot prices' directive and routes a related case (French industrial producer prices) to the xynaptic-catalog sibling. It lacks explicit exclusions for boundary cases like energy-price, which also covers gas/coal, so routing against that sibling is left to inference.

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?

No annotations are provided, so the description carries the full burden. It usefully discloses the pricing model ($0.020 per call, x402/USDC) and the returned field set, but says nothing about rate limits, authentication beyond payment, error behavior, or freshness/latency of the registry data.

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?

Dense but front-loaded: resource and field list first, then parameter, usage, and price. Every clause adds information, though the em-dash field enumeration is slightly list-like and could be trimmed without loss.

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 appropriately enumerates the identity fields an agent can expect. Combined with the SIREN usage note and pricing, it is complete enough to call correctly, with only error/edge-case behavior left unaddressed.

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%, so the baseline is 3. The description flags the ?siren= parameter and its role as the required lookup key, but does not mention the other documented params (naf, name, status) or clarify that siren must be a 9-digit SIREN rather than a SIRET.

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 (consolidated French state-registry company identity) and enumerates the exact fields returned (SIREN, legal form, NAF, headcount, HQ, VAT, creation, status). It explicitly differentiates from the sibling company-search by positioning itself as the downstream identity lookup keyed on a SIREN.

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?

Provides clear usage context: "Use with a SIREN from company-search for registry identity, status and financial strength of a French company". It names the sibling that produces the input key and states the intent, but gives no explicit when-not-to-use or handling for an invalid/unknown SIREN.

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?

No annotations are provided, so the description carries the full burden. It does add a genuinely useful behavioral trait absent from structured fields — the cost and payment mechanism ('$0.020 per call, x402/USDC'). However, it discloses nothing about auth requirements, rate limits, or the shape of results beyond three field labels, so gaps remain for a no-annotation 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?

Front-loaded and reasonably short, but 'find a company by name or SIREN' is stated twice and the 'funnel' framing is repeated in both the positioning and usage sentences. Some redundancy that does not earn its place.

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 should carry more. It names three result fields and the cost, but gives no return structure, result count semantics, or auth context. Adequate for routing, incomplete for invocation confidence.

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%, so the baseline is 3. The description adds a syntax hint ('Parameter: ?q=name'), but the schema's own properties are top/query/results with a required 'q' — the description does not clarify this naming mismatch or explain top/results, so it adds only marginal 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?

States a specific verb and resource (find French companies) and the fields surfaced (identity, activity, status), and distinguishes itself from the sibling company-profile by positioning itself as the entry point. An agent can tell it apart from company-profile without opening either schema.

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?

Explicitly names the workflow: 'First step of the French company funnel: find a company by name or SIREN, get its SIREN, then call company-profile for the full dossier.' The when-to-use condition and the follow-up alternative are both stated, leaving nothing to inference.

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?

No annotations are provided, so the description carries the full burden. It discloses the upstream data source (CoinMarketCap Pro), the GET/no-param access pattern, and the cost/payment model ($0.010 per call, x402/USDC) — meaningful behavioral context an agent needs before invoking. It does not cover caching, rate limits, or error behavior, keeping it below 5.

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 return fields, then usage, then a bracketed pricing note. Slightly redundant in restating the market-cap/dominance/volume list twice, but no filler sentences.

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, and the description compensates by enumerating the returned metrics and naming the data source. With the pricing and usage context included, an agent has enough to call it correctly; only pagination/refresh semantics are absent.

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 baseline is 3. The description usefully clarifies 'GET (no params)', which corrects the misleading schema where the nested 'params' properties (btc_dominance, total_mcap_usd_t, volume_24h_usd_b) are actually response fields, not inputs. It adds clarification but no syntax or format detail beyond that.

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+resource ('global crypto market snapshot') and enumerates exactly what it returns: total market cap, BTC/ETH dominance, 24h volume, trend assessment. This clearly distinguishes it from siblings crypto-price (single asset) and crypto-movers (top movers).

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 for overall crypto market context' plus 'Combine with crypto-movers to identify what drives the market' gives an explicit use case and names a sibling for a complementary task. It stops short of stating an exclusion, e.g. that crypto-price should be used for a single coin, so it is clear but not fully routing.

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 does well: it discloses the data source (CoinMarketCap Pro), the liquidity/volume filtering behavior, the HTTP method and limit, the return contents (prices and % changes), and the cost/payment model ($0.020 per call, x402/USDC). It omits pagination, rate limits, and exact failure behavior, so it falls short of a 5.

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, scope, usage, alternative, and pricing are front-loaded and each sentence carries signal. Slightly padded by marketing phrasing ('clean lists of real movers, not low-liquidity scams') that partly repeats the usage line.

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 annotations and no output schema, the description still covers purpose, filtering rationale, sibling routing, payment, and return contents. It leaves the nested params semantics and error/pagination behavior underspecified, which is the main remaining gap.

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 nominally 100% but the property 'descriptions' are only example snippets, so they clarify little. The description adds just 'GET (?limit=5)', which does not reconcile the schema's odd top/losers/gainers parameters, so baseline 3 is warranted.

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+resource: fetch top 24h gainers and losers, filtered by volume, sourced from CoinMarketCap Pro. It explicitly names the sibling it is not (crypto-price for a single-asset snapshot), so an agent can route without opening the schema.

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 an explicit when-to-use ('find 24h gainers and losers, liquidity-filtered, anti low-cap scams') and names the alternative and the condition that selects it ('For a single asset snapshot use crypto-price instead'). No inference required.

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 does disclose genuinely useful traits beyond the schema: USD-only scope, a $0.010 per call cost, and x402/USDC payment rails. It says nothing about caching/latency, rate limits, or what the response payload looks like, so gaps remain for a paid endpoint.

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 resource and covered assets, then usage, then pricing – a sensible information hierarchy in three compact segments. Branding ('Xynaptic') and the bracketed price tag add a little noise but no full sentence 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, so the description must stand alone; it compensates by enumerating the payload contents (prices, 24h changes, breadth, top movers) and the currency constraint. Remaining gaps – return shape specifics and error/payment-failure behavior for a paid call – are minor for a read-only snapshot.

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%, so the baseline is 3. The single 'params' object and its nested 'market'/'prices' entries are documented only by example strings in the schema, and the description adds no interpretation of them (notably it never explains that these read like output fields rather than inputs).

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: a real-time snapshot of six named crypto assets (BTC, ETH, SOL, ADA, LINK, DOT) with 24h changes, breadth and top movers, and pins the currency to USD. It explicitly routes to crypto-global for market context, but never addresses the overlap with the sibling crypto-movers, even though it advertises 'top movers' as part of its own payload.

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 for a real-time price snapshot of major crypto assets' gives a clear positive trigger, and 'For market context use crypto-global' names an explicit alternative with the condition that selects it. Only crypto-movers, the closest sibling given the 'top movers' feature, is left unaddressed.

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 load and discloses meaningful traits beyond the schema: official RTE source, quarter-hourly freshness, GET semantics, and pay-per-call pricing ($0.020 via x402/USDC). It does not state rate limits, error behavior, or geographic/historical coverage boundaries, but the behavioral picture is solid for a read-only data fetch.

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-loads the resource and data source, then the return fields, freshness, request format, usage, and price in a compact run of sentences. It is slightly repetitive, restating 'RTE official' and re-listing the output fields in the Usage sentence, but no sentence is expendable.

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 annotations and no output schema, the description adequately covers source, freshness, output fields, request format/default, and pricing. Missing only edge details like rate limits, error handling, or whether non-today dates are backfilled, which are minor for a read-only endpoint.

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 baseline is 3, and the description does add the date format (YYYY-MM-DD) and default (today). However, the nested schema lists response-style fields (consumption_mw, forecast_mw, co2_g_kwh, nuclear_mw) as input properties, and the description never clarifies these should not be passed, leaving a real semantic ambiguity unresolved.

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?

Names a specific resource (live French electricity demand from RTE éCO2mix) and enumerates exactly what it returns: consumption MW, day-ahead forecast, CO2 rate, and generation mix. This is distinguishable from siblings like energy-price and energy-interconnections without opening any schema.

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 explicit 'Usage: Use for live French electricity consumption, forecast, CO2 rate and generation mix' sentence tells the agent when to reach for this tool, and the 'live/quarter-hourly' framing implies it is for current rather than historical data. It stops short of naming alternatives such as energy-price for market pricing, so there is no explicit when-not guidance.

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?

No annotations, so the description carries the full burden; it discloses the HTTP method (GET), the query format, and crucially the cost model ($0.020 per call, x402/USDC payment). It omits return payload shape and rate-limit/error behavior, but the payment context is unusually valuable information that an agent otherwise could not infer.

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?

Dense but front-loaded: resource identification, then endpoint, then routing rules, then exclusions. Every clause is functional, though the bracket-tagged pricing note reads as an afterthought appended to the end.

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 supplies the endpoint pattern, coverage, cost, and routing alternatives. It does not sketch the response shape or units (MW, net imports), which is a minor remaining gap for a data-retrieval 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%, so the baseline is 3. The description reinforces the date format ('?date=YYYY-MM-DD') and mentions ?region=fr for the sibling tool, adding only marginal detail 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?

States a specific verb+resource (live cross-border power flows) with scope (France, RTE official) and enumerates the exact borders covered. An agent can immediately distinguish it from energy-demand and energy-price without opening the schema.

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?

Explicitly routes to alternatives: 'for French consumption or generation mix use energy-demand; for spot prices use energy-price'. Also states a negative condition ('Not for non-French borders — this covers France only'), so both when and when-not are covered.

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?

No annotations are supplied, so the description carries the full behavioral burden. It does disclose two genuinely useful traits the schema cannot convey: the data source (awattar) and a cost/payment model ('$0.020 per call, x402/USDC'), which matters because the agent may need to handle payment. However, it says nothing about refresh cadence, caching, rate limits, or failure behavior, so the disclosure is partial.

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 short and front-loaded: purpose first, then usage and the alternative tool, then cost. Every clause is largely functional. Minor waste in the branding prefix and the vague 'Lightweight, real-time' filler, which is marketing rather than actionable specification.

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 characterize returns, and it does: spot price in EUR/MWh, a 24h trend, and an hourly profile. Combined with the region and pricing notes, an agent has enough to invoke it correctly. It could still be tighter on the exact response shape and the full set of valid region codes.

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%, so the baseline is 3. The description adds some value by naming the accepted bidding zones ('France, Germany-Luxembourg') where the schema only gives the example 'de'. Notably, the schema's other properties (price_eur_mwh, change_24h_pct) look like response fields rather than inputs, and the description does not clarify this ambiguity.

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 resource and unit ('current electricity spot price (EUR/MWh) with 24h trend and hourly profile'), plus the data source (awattar) and coverage region (Europe), so an agent knows exactly what it returns. It also implicitly separates itself from energy-demand by naming that sibling for a different use case. It falls short of 5 only because the 'Xynaptic Energy Price' prefix is branding rather than specification.

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?

Explicit routing guidance is present: 'Use for day-ahead electricity spot prices per bidding zone (France, Germany-Luxembourg)' and 'For French live demand prefer energy-demand.' This tells the agent both the intended case and the alternative for an adjacent case. There is no statement of when *not* to use it at all, and 'current' vs 'day-ahead' is slightly ambiguous, keeping it below 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 and does well: it discloses cost ($0.020 per call), the payment mechanism (x402/USDC), that it is read-only ('spot rate only, no execution'), and the shape of the response. It omits auth/rate-limit details, which keeps it just short of a 5.

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-loads purpose, then usage, exclusion, and price in tight sentences with no filler. Slightly dense with four distinct clauses, but each clause carries decision-relevant 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?

No output schema exists, but the description enumerates the returned fields (spot, spread, fees, verdict), so an agent knows what comes back. Combined with the rail enumeration and cost, the definition is complete enough to call correctly, with only minor gaps around error/empty-currency behavior.

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%, so baseline is 3. The description confirms the query form ('GET ?currency=EUR') and adds a concrete currency example, but the schema's other properties (best_rail, coinbase_spot, rail_fees_usd) look like response fields rather than inputs and are not clarified beyond the schema text.

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/resource ('best fiat↔USDC conversion rate per payment rail') and enumerates the return content (Coinbase spot, spread, network fees, best-rail verdict). It explicitly distinguishes itself from the sibling crypto-price tool, so an agent can route correctly without opening a schema.

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 explicit when-to-use ('when settling x402 payments in a chosen rail'), an alternative ('For the price of a crypto asset use crypto-price instead'), and a when-not ('Not a currency-exchange service — spot rate only, no execution'). All routing conditions are stated, not inferred.

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 disclose provenance ('Open data (Wikidata)') and a paid-call cost model ($0.020 per call, x402/USDC) — non-obvious traits an agent should know before calling. It implies a read-only lookup but doesn't state rate limits, auth requirements, or failure behavior for unknown codes, 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?

Front-loaded with the resource and scope, then examples and the alternative, then price. Efficient overall, though the branded prefix 'Xynaptic Flight Airport Brief' and the bracketed pricing tag add some non-essential framing.

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 appropriately enumerates the returned fields, plus data source and cost. For a simple single-parameter lookup this is largely complete; only error/empty-result behavior for unrecognized codes is absent.

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 baseline is 3. The description adds meaning beyond the schema by clarifying that 'q' accepts either an airport name or an IATA code, which the schema only shows via a bare 'CGK' example. It does not explain the optional iata/icao/coord/country alternates, keeping it from a 5.

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 (airport fact sheet) with an explicit scope ('any airport worldwide by name or IATA code') and enumerates the returned fields: country, IATA/ICAO codes, coordinates, elevation, runway count, airlines. It also names the sibling it is not (notam-brief), so an agent can separate it from other lookup tools without opening the schema.

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 'Usage:' sentence states the positive case (airport fact sheet by name or code) and the exclusion with an explicit alternative ('For official FAA NOTAMs use notam-brief'). Concrete example calls (?q=Jakarta, ?q=CGK) further remove ambiguity about invocation.

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?

No annotations are supplied, so the description carries the full disclosure burden. It helpfully adds the cost model ($0.020 per call, x402/USDC) and the GET query-string form, but says nothing about result limits, pagination, rate limits, or auth/payment failure behavior.

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 identity and scope, then the example query and pricing in a compact run-on sentence. Nearly every clause earns its place, though folding price and query syntax into one line makes it slightly dense.

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, so the description must cover both behavior and returns. It lists the data facets returned (status, phase, eligibility, contacts) but omits response shape, limit defaults, and error/payment handling for a nested-object tool, leaving meaningful 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?

Schema description coverage is 100%, so the baseline is 3. The description adds the concrete query form (?condition=diabetes...) but also advertises &drug=&status=&limit= parameters that do not exist in the schema, which adds confusion rather than clarity beyond the structured fields.

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 (search) and resource (worldwide clinical trials via ClinicalTrials.gov) and enumerates the retrievable facets (status, phase, eligibility, contacts). No sibling tool covers clinical trials, so differentiation is moot and the purpose is unmistakable.

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 to search ClinicalTrials.gov by condition or intervention" gives clear invocation context and names the two accepted lookup keys. It stops short of stating when not to use it or pointing to any alternative, but with no sibling overlap there is little to exclude.

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?

No annotations are provided, so the description carries the burden. It discloses the source set (Légifrance, service-public.gouv.fr, ministères), the returned fields (titles, links, snippets), the 'not legal advice' caveat, and the payment model ($0.020 per call, x402/USDC). It does not state rate limits, result caps, or error behavior, but the safety/auth context is well covered for a read-only lookup 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?

Front-loaded with the core purpose and the routing rule, then supporting detail. The em-dash-heavy packing and embedded pricing make it slightly dense, but 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?

With no output schema, the description usefully names the returned fields and source set, and it covers exclusions and cost. Minor gaps remain around result count defaults and source filtering, but the agent has enough to invoke it 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% for the single required 'q' parameter, so the baseline is 3. The description adds a usage example (GET ?q=licenciement) but no syntax, encoding, or source-filter guidance beyond what the schema already documents.

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 — 'French legal watch by keyword' over official sources — and enumerates the covered domains (labor law, contracts, regulations). It also names the sibling it is not for (company-profile), so an agent can distinguish it without opening the schema.

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?

Explicit routing: 'Use ONLY for French law research by keyword' and 'For a company's dossier use company-profile instead.' Both the when and the when-not, plus the named alternative, are present.

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 delivers real behavioral context: refresh cadence (every 2 hours), the filtering rule (importance >= medium), deduplication/verification behavior, and cost ($0.020 per call, x402/USDC). It falls short of describing return shape or auth mechanics, but the pricing and freshness disclosures are genuinely useful beyond 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with identity and version, then scope, then usage routing, then pricing — a sensible information hierarchy. Slightly padded by the marketing line 'The executive briefing for busy agents,' which does not inform invocation, keeping it below 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?

For a news-digest tool with no annotations and no output schema, the description covers freshness, filtering, aggregation, alternatives, and price — enough to call it correctly. The main omission is any hint at the return payload, which an agent would only learn at runtime.

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% and the params object is described there, so the baseline is 3. The description's only param-relevant contribution is the 'Refreshed every 2 hours' line loosely mapping to the refresh parameter; it adds no syntax or format meaning 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?

States a specific resource (curated global events) with concrete qualifiers: importance >= medium, aggregated from all 7 feeds, cross-feed deduplicated. It explicitly distinguishes itself from the regional/thematic digests (news-eu, news-us, news-asia, news-tech) that exist as alternatives, so an agent can route without opening another schema.

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 an explicit selection rule: 'Use for a broad world news digest when the scope is global or unknown,' and names the alternative discovery path (xynaptic-catalog) for narrower scopes. Both the when-to-use and the when-to-look-elsewhere conditions are stated.

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: the operation is a GET read, results are active notices with effective dates in both plain text and ICAO format, and access is metered ($0.020 per call, x402/USDC). It omits rate limits, cache/freshness, and error behavior, but the payment and read semantics are well covered.

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 resource, then example, then usage routing, then price — a sensible order with no wasted preamble. It repeats the FAA/US-airport framing twice ('official FAA NOTAMs' in both the opening and the Usage line), which is mild 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?

There is no output schema, so the description must describe returns, and it does (active notices, effective dates, plain text plus ICAO format). Combined with the disclosed payment model and the alternative-tool pointer, an agent has enough to invoke and interpret it, though freshness and failure behavior are unstated.

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%, so the baseline is 3. The description adds only an example call (GET ?location=KDFW) and confirms location is a US airport code; it says nothing about the 'notams' and 'critical' fields visible in the schema, so it neither compensates for nor deepens parameter meaning.

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+resource (retrieves official FAA NOTAMs for any US airport) and names the distinguishing scope (US airport codes, active notices with effective dates). It also explicitly routes the radius-search case to 'notam-area', so the agent can separate it from the other aviation tool without opening a schema.

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?

Explicitly says to use this for official FAA NOTAMs by US airport code (KDFW...) and points to notam-area via xynaptic-catalog for radius searches around any point. This gives both a use case and an alternative, though the indirect routing through the catalog tool is slightly awkward rather than a clean sibling pointer.

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 load and does reasonably well: it discloses the data source (official DVF registry), the return statistics (median, mean, min/max, volume), and the commercial/behavioural facts an agent must know before calling — $0.020 per call via x402/USDC. It does not cover error behaviour, coverage gaps for small communes, or latency.

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 resource and scope, then example, then usage routing, then price. Every element is relevant, though "Robust median" is redundant with the earlier list of statistics and the description is dense enough that it reads as a tagline stack rather than prose.

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 usefully enumerates what comes back (median, mean, min/max, volume) and identifies the upstream registry, plus cost and payment mechanism. For a single-parameter query tool this is close to sufficient; only error and coverage caveats 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?

Schema description coverage is 100%, so the schema already documents city (required) and type, making the baseline 3 appropriate. The description adds a concrete example call (GET ?city=Paris&type=Appartement), but it does not resolve the oddity that median_price_m2 is exposed as an input property when it reads like a response field, nor does it explain that type is optional.

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 (median price per sqm), a precise scope (French commune + property type), and the data provenance (real DVF transactions). It is clearly distinguishable from siblings like crypto-price, commodity-price, energy-price and security-price because the asset class is named up front.

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 for French real-estate price benchmarks by city and type" gives clear intended context, and it routes the agent to xynaptic-catalog for rents, valuations and yields. It stops short of an explicit when-not-to-use rule for this capability, but the domain boundary is unambiguous.

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?

No annotations are provided, so the description carries the full burden, and it does substantial work: it discloses freshness ('Refreshed intraday'), the data sources composition (price/close/ranges/currency/exchange/OHLCV), the history cap (up to 30d), and — unusually — the cost and payment rail ('$0.010 per call, x402/USDC'), which matters for an agent deciding whether to invoke. It does not state error behavior for invalid tickers or rate limits, so it falls short of a 5.

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 tool's identity and payload, then invocation example, freshness, usage/exclusion, and pricing. Every clause earns its place except the pricing tag, which could arguably live in structured metadata. It is dense but not 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?

For a single-required-param fetch tool with no output schema, the description supplies exactly what an agent needs: what comes back, freshness, a call example, the domain boundary (US-only), the sibling route for EU, and cost. The only omissions are error/rate-limit behavior, which is minor given the tool's simplicity.

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 baseline is 3, but the description adds meaning beyond the schema: the 'history=30' example gives the syntax and semantics of a history parameter (value = number of days, max 30) that is not in the schema, and the 'ticker=AAPL' example confirms the expected format. The stray price/previous_close/week52 fields in the schema are output-like, not user parameters, and the description clarifies the tool is invoked by ticker + history.

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/resource ('Security Price — spot market data for US stocks') and enumerates exactly what is returned: price, previous close, day range, 52-week range, currency, exchange, plus 30d OHLCV history. It explicitly excludes EU-listed stocks and points to the sibling tool (xynaptic-catalog → security-eu-price), so an agent can distinguish it from commodity-price, crypto-price, and property-price without opening any schema.

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?

Explicit when-to-use ('Use for a US-listed stock price with optional history') and an explicit when-not-to-use with the alternative named ('EU-listed stocks are available through the xynaptic-catalog tool'). It even provides a concrete invocation example (GET ?ticker=AAPL&history=30). Nothing is left to inference.

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 does disclose genuinely useful behavioral facts: the HTTP verb (GET), the cost ($0.020 per call) and the payment protocol (x402/USDC). However it says nothing about return format, pagination/limit behavior, or rate limits, leaving meaningful gaps for a paid network call.

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 resource identity and a concrete query example. Slightly redundant, repeating 'French public procurement notices (BOAMP)' in the Usage sentence, but overall compact and free of 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?

For a search tool with no output schema and a nested 'params' object, the description covers what to query but not what comes back or how paging works. The embedded total/notices fields hint at the response shape but neither description nor schema explains them, so an agent lacks a complete picture.

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 nominally 100%, but the schema only exposes 'q' (with a bare example), while the description surfaces the additional query parameters buyer, dept, days and limit via the example URL. That is real semantic value beyond the schema, though their formats (e.g., department codes, default days) remain unexplained.

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 (search) and a precisely scoped resource (French public procurement notices / BOAMP), plus the facets available (keyword, buyer, department). No sibling tool covers tenders or procurement, so an agent can immediately route here without opening the schema.

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 'Usage:' sentence restates the purpose ('find notices by keyword, buyer or department') rather than naming a when/when-not condition or an alternative to prefer. Usage is implied by the domain, but there is no explicit guidance on limits, exclusions, or which sibling (if any) to use instead.

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 does well: it discloses the returned credentials (ticket_id|ticket_secret), validity window (90 days), the X-TICKET header for consumption, the amount range (0.5-20 USDC), and the x402/USDC payment protocol. It omits failure/expiry behavior and any wallet/auth prerequisites, keeping it short of a 5.

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?

Information is front-loaded with the value proposition first, then mechanics, then usage. It is dense but mostly earns its words; the trailing '[price: $0.500 per call, x402/USDC]' partially restates pricing already implied earlier, a 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?

For a payment tool with no output schema and no annotations, the description covers purpose, cost, return values, validity, and consumption mechanics. It stops short of explaining error/expiry handling or minimum-cost semantics, but an agent has enough to invoke it 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?

Although schema coverage is nominally 100%, the body schema is an empty object, so the schema itself documents nothing useful. The description compensates by supplying the real parameter — 'POST { amount_usd }' with a 0.5-20 USDC range — which the agent could not otherwise infer.

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 and resource: buy/preload a 'Xynaptic Ticket — prepaid credit' for later service consumption at a 20% discount. An agent can distinguish it from the data-oriented siblings (crypto-price, company-search, etc.). It is clear, though the core verb is implied rather than stated outright.

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?

It gives explicit usage context: 'Use to preload a prepaid ticket at -20% for cost-efficient batches of calls,' telling the agent when this tool is worthwhile. There are no genuine alternatives among the siblings to name, so the absence of exclusions is acceptable, though it does not quantify when batching pays off.

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 important traits: HTTP method and query shape ('GET ?from=...&to=...'), plus a cost and payment model ('$0.030 per call, x402/USDC'), which tells the agent an auth/payment step is involved. It stops short of stating rate limits, error behavior, or whether results are cached/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?

Front-loaded with the core capability, then usage constraints, then price – a sensible priority order with no filler sentences. It is dense with parenthetical asides and a parameter example that make it slightly harder to scan than a 5 would require.

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 correctly enumerates what comes back (times, duration, transfers, best pick), and it covers cost, payment, and routing alternatives. It is nearly complete; the only gap is that the schema's 'best' and 'journeys' fields are never clarified.

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 baseline is 3, but the description adds a concrete call example and reveals date/time query parameters that do not appear in the schema's property list, which is genuinely new information. It does not, however, explain the schema's opaque 'best' and 'journeys' fields, and the date/time mention creates a slight mismatch with the declared 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?

States a specific verb+resource+scope: 'full train itinerary between two French stations or cities (SNCF)' with the returned fields (times, duration, transfers, best pick) named. The 'FR / SNCF' scoping separates it cleanly from the generic travel-style siblings like flight-airport-brief.

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?

Explicit when-to-use ('Use ONLY for French train journeys'), when-not ('Not for non-French rail networks'), and a named alternative for adjacent needs (road itineraries and city overviews via xynaptic-catalog). Nothing is left to inference.

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 does add real behavior: GET services take {service, params}, POST services take {service, body}, and 'the x402 challenge is surfaced for payment' tells the agent to expect a payment round-trip. It omits what happens after a challenge is satisfied (retry semantics) and the response shape, which keeps it below a 5.

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 core routing rule is front-loaded, which is good, but the GET/POST payload mapping is stated twice verbatim and the domain list is also duplicated between the opening and the usage sentence. The block is longer than it needs to be for the information conveyed.

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 gateway fronting 200+ services, the biggest missing piece is discovery: there is no mention of how an agent finds valid service names, so it must already know them (only three examples appear in the schema). Payment, mutation type, and payload shape are covered, but the naming/discovery gap is material.

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 the schema already states which service prefixes require body (ai-*, insurance-*, bank-*-parse, portfolio) and which take params. The description largely restates that mapping, adding no new syntax, naming conventions, or validation rules. Baseline 3 for a fully documented 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 concrete verb+resource ('access any of the 200+ Xynaptic services by name') and enumerates the covered domains (bank analytics, regional news, EU stocks, property, notam, AI agents). It also explicitly frames itself as a fallback gateway rather than a peer of the flagship tools, so an agent can place it against the siblings.

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 ONLY for a service outside the 23 flagship tools' plus 'For flagship tools call them directly by name' gives a clear selection rule and an exclusion. However, it never names the 23 flagship tools, and the category list following 'flagship tools:' repeats the categories the catalog is supposed to cover, which blurs the boundary the agent needs to apply.

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