Mode VC LLM Token Economics & Model Release Velocity

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description contributes real behavioral context beyond that: results are computed at list price, and the math plus a dated price file match the public modevc.com calculator, signalling deterministic parity rather than live market pricing. It does not state what the returned object looks like, but with no output schema an enumeration of computed figures partially covers that.

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 verb and the headline outputs, mapping closely to what the tool returns. The output enumeration is long but each item earns its place; the trailing "compares 9 common models" fragment is the only weakly justified clause and is arguably inconsistent with the enum.

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 10 optional parameters and no output schema, the description carries the burden of telling an agent what comes back, and it does so by listing all five result families. What's missing is shape-level detail, such as whether margin output includes a break-even price per user or how savings are broken out, which matters for a calculator with no declared return type.

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 schema already explains each parameter, including defaults. The description adds directional meaning by naming prompt-cache savings and routing a share of requests to a cheaper model, but it does not clarify inputs the schema leaves thin, such as the interaction between route_model and route_share_pct or how target_margin_pct changes the output. The odd claim "compares 9 common models" against a 20-value enum adds no usable parameter guidance.

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?

Opens with a specific verb ("Estimates") and enumerates the exact quantities produced: monthly LLM spend, cost per user and per request, prompt-cache savings, routing savings, and gross margin. It also scopes the domain to models from Claude, GPT, Gemini, DeepSeek and Grok, which cleanly separates it from the equity, dilution and SaaS-metric calculators in the sibling list.

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

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

The phrase "for an AI product on any current Claude, GPT, Gemini, DeepSeek or Grok model at list price" gives a clear condition for reaching for this tool. It stops short of naming when not to use it or pointing at a sibling (e.g., a generic burn/SaaS calculator), so it sits just below the explicit routing seen in the strongest definitions.

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?

Annotations already declare the safety profile (readOnlyHint, idempotentHint, destructiveHint=false, openWorldHint=false), so the description's job is lighter. It adds genuine context beyond them: the data is 'as published' on the chart page and original data must be fetched via source_urls — a useful fidelity caveat that tells the agent the returned table is a snapshot, not a live source.

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 tight sentences: the return payload first, then the lookup mechanism and the data-fidelity caveat. No filler, no restatement of the title, and the most important information is front-loaded.

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 carries the burden of describing returns — and it does so explicitly, naming every returned field plus the source_urls follow-up. For a single-parameter, read-only lookup already covered by annotations, nothing an agent needs to invoke it correctly is 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 coverage is 100% and there is only one parameter, so the schema already documents that slug accepts a slug or title. The description's 'Look up by slug or title' largely restates the schema description rather than adding format, normalization, or failure-mode details (e.g., what happens on an ambiguous title match). 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?

States a specific verb and resource — 'Returns one chart from the Chart of the Day archive' — and enumerates exactly what comes back (data table, claim, takeaway, note, sources). The single-chart, slug-or-title lookup framing distinguishes it from the search_charts sibling, so an agent can route correctly 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?

'Look up by slug or title' implies the usage context (you already know which chart you want), which is a reasonable implicit contrast to search_charts. However, no sibling is named and no condition is stated for choosing this over search_charts when only a title fragment is known. Usage is implied, not guided.

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?

Annotations already declare readOnly, idempotent, non-destructive and closed-world behavior, so the safety profile is covered. The description adds genuine context beyond that: the one-new-chart-per-day cadence, the topic mix (AI model, adoption, developer-package, pricing data), and the exact fields included per chart.

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 sentence that front-loads the resource and recency, then packs cadence, ordering, scope and return fields without a wasted word.

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 no-required-parameter read tool with no output schema, the description covers what the tool returns and how results are ordered, which is the essential missing structured information. It does not mention pagination beyond the limit or error/empty-archive behavior, but little else is needed.

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 single limit parameter is fully documented in the schema, including its default of 3 and 1-10 bounds. The description adds no additional meaning about the parameter, so the 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?

The description names a specific verb and resource (returns the most recent charts from the Mode VC Chart of the Day archive), states ordering (newest first), and enumerates returned fields (claim, takeaway, URL, sources). It implicitly separates itself from search_charts by emphasizing recency rather than querying, but never names a sibling 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?

Usage is implied by 'most recent charts' and the default limit of 3, so an agent can infer this is the browse-latest entry point. However, there is no explicit statement of when to prefer this over get_chart_data or search_charts, and no exclusions or prerequisites are given.

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?

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world). The description adds genuine context beyond them: what the result contains (claims, takeaways, canonical URLs, markdown endpoints, cited sources) and that the archive grows daily with the response reporting its current size.

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 compact sentences, front-loaded with the action and scope, then return contents, then freshness. No filler and nothing repeated from the structured fields.

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 read-only search tool with no output schema, the description covers scope, matching fields, returned artifacts, and data freshness. An agent has everything needed 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 coverage is 100%, so the baseline is 3, but the description earns a bump by clarifying what the free-text query actually matches against (title, claim, takeaway, keywords), which the schema alone does not say. The limit parameter is left entirely to 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 (Searches) and resource (every chart in the Mode VC Chart of the Day archive) plus the exact fields matched (title, claim, takeaway, keywords). The read/search framing implicitly separates it from the get_* siblings, but no sibling is named explicitly, so it stops short of 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?

Usage is implied: reach for this when you want to find charts by keyword rather than fetch a known one. However, it never says when to prefer it over get_chart_data or get_venture_chart_of_the_day, and there are no stated exclusions or prerequisites.

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