Nota

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 monitoring behavior, the 20-voice cap, the lexicon-scored sentiment, and convergence detection. However, it does not disclose whether this is a read-only operation, whether it creates subscriptions or side effects, rate limits, or what happens when voices are invalid or exceed 20. The description adds meaningful behavioral context but leaves the safety profile undisclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is a single sentence that front-loads the core action (monitor voices) and packs in the key outputs. It is efficient and every clause earns its place. It could be slightly more structured (e.g., separating the output metrics into a list), but it is not bloated or repetitive.

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 monitoring tool with no output schema and no annotations, the description covers the main inputs and outputs but omits important operational details: whether the tool is read-only, how results are returned (streaming vs. one-shot report), what happens if more than 20 voices are provided, and whether there are rate limits or authentication requirements. The sibling tools suggest a research context, but the description alone is not fully complete for an agent to invoke it without further assumptions.

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 all three parameters. The description adds context by explaining the voice ID format (tg:, bs:, x:) and the default behavior for tokens (every cashtag found), which goes slightly beyond the schema. However, it doesn't explain the hours parameter's default beyond the schema's 'default 24' or clarify edge cases like overlapping tokens. Baseline 3 is appropriate because the schema does the heavy lifting and the description 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?

The description uses a specific verb ('Monitor') and names the exact resource (up to 20 user-selected voices from Telegram public channels and X handles) and the outputs (tokens mentioned, lexicon-scored sentiment, conviction, urgency, convergence). It clearly distinguishes itself from the sibling tools (news_verify, price_crosscheck, technicals_crosscheck) by focusing on narrative convergence across social voices.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use this tool: when you need to track which tokens multiple voices mention and whether they converge on a narrative. It does not explicitly state when not to use it or name alternatives, but the sibling tool names and the description's focus on social voices make the use case clear. A small gap: no explicit exclusion like 'for price verification use price_crosscheck'.

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?

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It states the operations (counting sources, scoring, attaching evidence) but does not explicitly declare the tool as read-only, mention any side effects, auth requirements, rate limits, or the nature of the output beyond the implied 'side by side' view. This is a moderate level of transparency, covering the main actions but omitting potential operational details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is a single well-structured sentence that front-loads the primary purpose ('Verify a crypto news claim') and then efficiently lists the three key actions without redundancy. Every part of the sentence contributes to understanding the tool, and there is no wasted wording, earning a high score for conciseness and structure.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

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

Given that there is no output schema, the description provides a reasonable hint at the results (count of sources, corroboration score, and market evidence side by side). It also implies the integration with 'RYO analyze_token evidence,' which gives context on the data flow. However, it does not clarify the exact output structure or any prerequisites (e.g., token symbols needed), leaving some ambiguity for an agent expecting a precise return format. Still, it is fairly complete for a verification tool with simple parameters.

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

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

Schema description coverage is 100%, so the parameters are already well-documented in the input schema. The description adds some context (e.g., symbol related to market evidence, max_results for search results) but largely repeats what the schema states. It does not introduce new syntax or format details, so the value added over the schema is marginal, justifying the baseline score of 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 clearly states a specific verb ('Verify') and a resource ('a crypto news claim'), and distinguishes its action by detailing three concrete steps: counting independent sources, scoring corroboration, and attaching market evidence. This differentiates it from the siblings (narrative_convergence, price_crosscheck, technicals_crosscheck) which address other analytical angles, and leaves no ambiguity about what the tool does.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a clear context for use ('Verify a crypto news claim') and describes the workflow, so an agent knows when to invoke it. However, it does not explicitly mention when not to use it or name alternative tools for exclusions, so it falls short of the highest bar that includes direct comparisons to siblings.

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 behavioral burden. It discloses that this is a read-only fetch, names the data sources, states the aggregation (median/spread), and explains the comparison output. It does not mention rate limits or error behavior, but the core behavior is transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

A single dense, front-loaded sentence that packs in sources, computation, output, and the reference-price use case. Every clause adds information; 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?

The description explains what data is fetched, from where, and what is reported (median, spread, deviation flag). It does not specify output format or how optional reference params behave when absent, but the schema covers parameter basics and the overall usage is clear.

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 useful example ('e.g. RYO's') for the reference price and situates it in the comparison workflow, but does not substantially elaborate on reference_path or reference_fear_greed beyond 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?

The description is explicit and specific: 'Fetch independent USD spot prices... from CoinGecko, Coinbase and Kraken', then 'report median and spread', then 'flag how far a reference price deviates'. This clearly distinguishes it from the sibling news/technical/narrative 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?

The intended use case is clear: cross-check a reference price against independent exchange prices and surface deviation. It doesn't explicitly name alternatives or say when not to use it, but the price-comparison context is evident from the description.

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 provided, the description carries the full burden and largely succeeds: it discloses the data source, daily candle frequency, Wilder smoothing, the recalculated metrics, and the comparison-style output. It does not cover failure modes, rate limits, or exact output formatting, but the core read-only calculation behavior is transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is a single dense sentence with no filler. The main action and target metrics come first, followed by the data source and the comparison behavior. Every clause earns 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?

The description is sufficient for an agent to understand what the tool computes, from where, and roughly what it returns. There is no output schema, so a bit more detail about how the deviation is reported or what happens when reference values are omitted would make it fully complete, but the core invocation context is covered.

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 interpretive value by explaining that RSI and ATR are computed with Wilder smoothing on daily CoinGecko OHLC and that reference values are compared against this independent calculation. This helps the agent understand how days and reference parameters interrelate beyond their raw schema descriptions.

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 operation ('recompute') and resource (RSI(14), ATR(14), 1d/7d/30d performance) with a clear data source (CoinGecko public OHLC). It also implicitly distinguishes itself from siblings like price_crosscheck by focusing on technical-indicator verification rather than price alone.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly establishes the intended use case: independently recomputing technical indicators and measuring how far reference values drift from them. It does not explicitly state when-not-to-use or name alternatives, so it misses a top score, but the context is clear enough for an agent to select it appropriately.

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