Free creator rate benchmarks and brand-offer checks, with sources. No login, no API key.
Scored across 2 tools
The two tools have clearly distinct purposes: selah_check_offer evaluates an existing offer against rates, while selah_rate_benchmark provides a rate to charge when no offer exists. Their input requirements (offered amount vs. no amount) and use cases ('is this fair' vs. 'what should I charge') are unambiguous, despite some overlap in output information.
Both tools use a consistent snake_case pattern with the same 'selah_' prefix followed by a two-word descriptive name. The style is uniform and predictable.
With only 2 tools, the count is slightly below the typical 3–15 range, but both tools are well-scoped and earn their place for this narrow domain. The set covers the core functions without redundancy.
The surface covers the essential operations for brand deal rates: checking an offer and benchmarking a rate. The descriptions explicitly exclude out-of-scope areas (contracts, legal/tax advice, finding brands), so no obvious gaps remain for the stated purpose.
Is this brand offer fair? Give what the brand wants (deliverable, the creator's audience size, any usage rights, exclusivity or rush) and the amount they offered, and get where the offer sits against Selah's rate for that whole scope, how it compares with published rates, and a suggested counter-offer. Use for 'a brand offered me $400 for a Reel, is that fair', 'should I accept this offer', 'what should I counter with'. Needs a deliverable, an audience size and the offered amount: ask the creator for any that are missing rather than guessing. Does not review contracts, give legal or tax advice, find brands, or price UGC, affiliate or commission-only work. Amounts are in US dollars. No account needed.
| Name | Required | Description | Default |
|---|---|---|---|
| rush | No | The brand needs it faster than a normal turnaround | |
| perpetual | No | The brand wants usage forever / in perpetuity | |
| usage_type | No | Usage rights the brand wants, if any. organic = the brand reposts it on their own pages; paid = the brand runs it as ads from their own account; whitelisting = ads run from the creator's handle (includes TikTok Spark Codes); ooh = out-of-home | |
| deliverable | Yes | What the brand wants: instagram_reel, instagram_post, instagram_story (a set), tiktok_video, youtube_integration (a mention inside a video), youtube_dedicated, podcast_mention, blog_post or newsletter_feature | |
| usage_months | No | How long the usage lasts, in months (30 days = 1, 90 days = 3). Defaults to 3 when usage_type is set and the brand named no term | |
| audience_size | Yes | The creator's audience on the platform the deliverable is for: followers, subscribers or listeners | |
| offered_amount | Yes | The total the brand offered for everything they're asking for, in US dollars | |
| exclusivity_months | No | Months the creator may not work with competitors. 0 or omitted = none |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, non-destructive and closed-world, so the bar is lower. The description still adds real context: no account required, amounts in USD, and an 'ask rather than guess' instruction for missing required inputs. It stops short of describing output structure, but most return detail is implied by the description itself.
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 core question and the returns, then the inputs, then exclusions. Sentence count is moderately high and some phrasing is conversational, but each sentence carries selection or invocation information and none is 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?
With no output schema, the description still tells the agent what comes back (rate comparison, published-rate comparison, suggested counter-offer), which inputs are mandatory, and how to handle gaps. Complete for an 8-parameter analytical 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 coverage is 100% including enum value explanations, so the baseline is 3. The description adds scope framing ('the whole scope', audience size, usage rights, exclusivity or rush), the USD unit, and the default/omission conventions, giving modest value 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 concrete verb and resource ('check a brand offer', get where the offer sits against Selah's rate, how it compares with published rates) and clearly contrasts with a pure rate lookup by centering on evaluating a specific received offer. An agent can select this over selah_rate_benchmark 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?
Gives example utterances ('a brand offered me $400 for a Reel, is that fair'), states the required inputs, and explicitly excludes contracts, legal/tax advice, brand finding, and UGC/affiliate/commission-only pricing. When-to-use and when-not are both spelled out.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
What should a creator charge for a sponsored post or brand deal? Give the deliverable and the creator's audience size (and, if the brand asks for them, usage rights, exclusivity or rush) and get Selah's rate, the range published creator-rate guides give for the same thing, the add-ons priced on top, and a link to how the numbers are sourced. Use for 'how much should I charge for an Instagram Reel with 50K followers', 'how much to charge for usage rights', 'what is a fair rate for a TikTok at 25K'. Needs a deliverable and an audience size: ask the creator for them rather than guessing. Does not review contracts, give legal or tax advice, find brands, or price UGC, affiliate or commission-only work. Figures are in US dollars. No account needed.
| Name | Required | Description | Default |
|---|---|---|---|
| rush | No | The brand needs it faster than a normal turnaround | |
| perpetual | No | The brand wants usage forever / in perpetuity | |
| usage_type | No | Usage rights the brand wants, if any. organic = the brand reposts it on their own pages; paid = the brand runs it as ads from their own account; whitelisting = ads run from the creator's handle (includes TikTok Spark Codes); ooh = out-of-home | |
| deliverable | Yes | What the brand wants: instagram_reel, instagram_post, instagram_story (a set), tiktok_video, youtube_integration (a mention inside a video), youtube_dedicated, podcast_mention, blog_post or newsletter_feature | |
| usage_months | No | How long the usage lasts, in months (30 days = 1, 90 days = 3). Defaults to 3 when usage_type is set and the brand named no term | |
| audience_size | Yes | The creator's audience on the platform the deliverable is for: followers, subscribers or listeners | |
| exclusivity_months | No | Months the creator may not work with competitors. 0 or omitted = none |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive, closed-world. The description adds real value beyond those: output components (Selah rate, guide range, priced add-ons, sourcing link), currency (USD), and no-account-needed. It does not cover latency, caching, or error behavior, so it stops 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 core question and output contents before examples and exclusions. Slightly long, but every sentence carries information (inputs, examples, prerequisites, exclusions, currency, auth).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, so the description must describe returns – and it does, enumerating the four output components. Inputs, exclusions, and currency are covered. Minor gap: no guidance on handling audience sizes outside the 100–10M range or usage_months >12, which the schema enforces but the description does not mention.
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 every parameter is already documented in the schema. The description names the required pair and grouped optional concepts (usage rights, exclusivity, rush) at a high level, which is sensible framing but adds no syntax, units, or defaults beyond the schema. Baseline 3 applies when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource (returns Selah's rate, published-guide range, add-ons, and sourcing link for a sponsored deliverable). It also explicitly excludes adjacent domains (contracts, legal/tax, brand-finding, UGC/affiliate/commission work), which cleanly separates it from selah_check_offer.
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 concrete example queries ('how much for an Instagram Reel with 50K followers', 'usage rights', 'TikTok at 25K') and states prerequisites (deliverable + audience size) and the failure mode (ask the creator rather than guess). Exclusion list further disambiguates when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
selah_check_offerselah_rate_benchmarkPrice creator brand deals, look up a brand's public creator program, read niche rate benchmarks.
Creator discovery & analytics across YouTube, Instagram, TikTok (30M+) + brand/sponsor intel.
Find viral outlier posts on TikTok, Instagram and YouTube, pull creator stats, and crawl on demand.
Instagram analytics, media kits, bio link and brand deals for creators, from your AI assistant.
Add one secure layer between your agents and this server.
