Pandough is a flour-first dough engine exposed as a remote MCP server. Agents call plan_bake with a real flour, kitchen temperature, and oven; the engine returns ingredient grams, computed yeast, a proofing schedule, and a calculator link.
Get detailed information about a specific flour by its slug. Returns full profile including protein, W strength, hydration curve, maturation profiles, and description.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Flour slug (e.g. 'caputo-pizzeria', 'manitoba-oro') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already establishes that this is a safe read operation. The description adds value by disclosing the shape of the response, listing protein, W strength, hydration curve, maturation profiles, and description, so the agent knows what data will come back. It does not cover error or not-found behavior, but that is secondary here.
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 compact sentence delivers the action, the required input, and the expected returns without filler. The most important detail, that the lookup is by slug, 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?
For a simple one-parameter read-only lookup, the description covers the input contract and enumerates the main returned data categories. It omits possible error cases or response edge cases, but nothing essential is missing for an agent to successfully invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema fully documents the only parameter, slug, including length constraints and concrete examples. The description adds little beyond the schema, but with 100% schema coverage the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Get'), names the resource ('a specific flour'), and identifies the lookup key ('by its slug'). This also differentiates it from the sibling search_flours, which would be the natural choice when the slug is unknown.
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 'by its slug' gives clear context: an agent should call this only when it already has a specific flour slug. It does not explicitly mention the alternative search_flours, but the qualifying slug is enough to make the intended use case clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List the signed-in baker's scheduled and active bakes. The first call prompts the chat app to authorize a Pandough account (OAuth scope bakes:read). Needs Pro (or cancelled-Pro). Returns ids, flour, hydration, schedule, and a calculator session link.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Although readOnlyHint=true already indicates a read operation, the description adds valuable behavioral context: the first call triggers an OAuth authorization prompt, requires Pro or cancelled-Pro, and returns specific fields. These details go beyond the annotation and help the agent anticipate side effects and prerequisites.
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 compact and front-loaded with the core purpose. Every sentence adds necessary information: scope, authorization behavior, subscription requirement, and return contents. No filler or redundant detail.
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 compensates by listing the returned data (ids, flour, hydration, schedule, calculator session link). It also covers authorization and subscription prerequisites, making the tool fully usable without further lookup.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so there is no parameter ambiguity to clarify. The description still adds relevant context about the implicit signed-in baker scope, which is the closest thing to parameter semantics for this tool.
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 ('List') and a specific resource ('the signed-in baker's scheduled and active bakes'), clearly distinguishing it from sibling tools like list_recipes or plan_bake. The scope is precise and immediately actionable.
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 gives strong context for when to call it: it lists the signed-in baker's own bakes and notes the Pro requirement and OAuth prompt. It does not explicitly name alternatives or say when not to use it, but the usage context is otherwise clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List the dough recipe styles plan_bake supports. Call this when a user names a style (Neapolitan, classica, focaccia, bread, baguette) so you pass the correct recipeSlug instead of guessing — and so you never confuse a style word like 'classica' with a flour whose name contains it.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, and the description aligns with that by saying 'List.' It adds contextual value by explaining that the output feeds plan_bake's recipeSlug and by highlighting a common failure mode. No contradiction with annotations exists.
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 two sentences with no wasted words. The core action is front-loaded, and the usage trigger with examples follows directly. 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?
For a zero-parameter, read-only list tool with no output schema, the description is complete: it states what is listed, when to call it, and why it matters. The mention of recipeSlug makes the intended return value clear enough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. The description does not need to add parameter detail, and mentioning recipeSlug gives relevant output-related context without misleading about 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?
The description uses a specific verb and resource: 'List the dough recipe styles plan_bake supports.' It also differentiates itself from flour-related siblings by warning not to confuse a style word like 'classica' with a flour name, making the tool's scope immediately clear.
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 states when to call the tool: whenever a user names a recipe style, so the agent passes the correct recipeSlug instead of guessing. It also provides an implicit exclusion by warning against confusing style words with flours, but it does not name the alternative flour-search tool explicitly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a bake plan with ingredient calculations. Returns exact ingredient amounts in grams, an auto-sized yeast amount, a fermentation schedule, dough warnings, and a calculator URL the user can open in Pandough with all parameters prefilled. Before calling this, gather the inputs that actually determine a good plan (skip any the user already volunteered): (1) Strongly ask which oven model they use — call search_ovens and pass ovenSlug. If it is unlisted, ask for ovenType plus ovenMaxCelsius. Oven capability materially changes the hydration band and bake feasibility; if the user declines, proceed but say the result is not oven-adapted. (2) Which flour(s) do they have? — call search_flours to resolve a flourSlug (or pass flourBlend for a 2–5 flour cut, e.g. a Pulcinella base with 30% Manitoba); this is what makes hydration and warnings flour-aware. (3) Their room temperature and fridge temperature — pass roomTempCelsius/fridgeTempCelsius; temperature is the dominant driver of yeast amount and timing. (4) How they mix/knead (by hand, stand mixer, no-knead) — plan_bake does not return technique guidance, so pair it with a troubleshoot call for method advice. (5) Do they want a preferment? — pass preferment for a biga, poolish, tiga, cold/long biga, or a natural sourdough starter. The yeast figure accounts for the preferment, and the calculator link opens with the build already set up, so recommending a biga in prose without passing this param hands the user a direct dough. Don't interrogate a user who already gave a full brief.
| Name | Required | Description | Default |
|---|---|---|---|
| locale | No | UI locale for the returned calculator link (e.g. 'en', 'pl', 'it'). Defaults to 'en'. | |
| bakeTime | No | Target bake time as an ISO 8601 datetime (e.g. '2026-06-10T18:00:00Z'). Must be in the future. | |
| ovenSlug | No | Exact oven model slug from search_ovens. Strongly preferred: the engine uses the model's real ceiling and oven class to adapt hydration and bake feasibility. Mutually exclusive with manual ovenType/ovenMaxCelsius. | |
| ovenType | No | Oven class for an unlisted model. Pass together with ovenMaxCelsius; prefer ovenSlug when search_ovens finds the model. | |
| servings | No | Number of portions/pizzas (1–50, default: 4) | |
| flourSlug | No | Flour slug from search_flours (e.g. 'caputo-pizzeria'). For a single flour. Mutually exclusive with flourBlend. | |
| flourBlend | No | A weighted flour blend (2–5 flours summing to 100%), e.g. [{flourSlug:'caputo-pizzeria',percent:70},{flourSlug:'manitoba-oro',percent:30}]. Mutually exclusive with flourSlug. | |
| preferment | No | Plan the bake around a preferment (biga, poolish, tiga, sourdough…). The yeast figure returned accounts for it, and the calculator link opens with the build already set up. | |
| recipeSlug | Yes | Recipe type slug. Supported: neapolitan-pizza, classica-pizza, focaccia, artisan-bread, baguette (call list_recipes for the full list). When the user names a style like 'classica', pass that style here — do NOT confuse it with a flour whose name happens to contain the word. | |
| roomTempHours | No | Room-temperature fermentation hours (0–168). Pass BOTH this and fridgeTempHours to lock the split; naming only a total lets the engine choose. Room legs above 14h are clamped with a warning. | |
| ovenMaxCelsius | No | The real maximum temperature in °C for an unlisted oven (100–600). This is what constrains hydration and bake time. | |
| ovenMinCelsius | No | Optional minimum set temperature in °C for an unlisted oven. | |
| fridgeTempHours | No | Cold fermentation hours (0–168). Pass BOTH this and roomTempHours to lock the split. | |
| roomTempCelsius | No | The user's real kitchen temperature in °C (2–40). The single biggest driver of yeast amount and timing — ask the user for it. | |
| hydrationPercent | No | Target hydration percentage (40–110) | |
| fridgeTempCelsius | No | The user's real fridge temperature in °C (0–12). Ask the user for it when a cold ferment is involved. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint annotation, the description reveals important behavioral traits: results are oven-adapted only if an oven is supplied, unlisted ovens fall back to manual type/max temp, the yeast figure accounts for preferment, then calculator link is prefilled, and room legs above 14h are clamped with a warning. This goes well beyond what annotations express.
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 long, but it earns its length for a 16-parameter tool with a nested preferment object. It front-loads the outcome, then uses a numbered preparation checklist that is easy for an agent to follow. A small amount of redundancy with schema descriptions is present, but 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 covers what the tool returns, how to source each decision-critical parameter, what to do when data is missing, and how to route related requests to siblings. Despite having no output schema, the return values are explicitly named, so an agent has enough context to call the tool 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?
Even though the schema already covers all parameters, the description adds meaning the schema cannot: ovenSlug is strongly preferred because it changes hydration band and feasibility, roomTempCelsius is 'the single biggest driver' of yeast and timing, and preferment must be passed as a parameter to avoid handing the user a direct dough. This substantially enriches 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?
The description leads with a specific verb and resource: 'Create a bake plan with ingredient calculations' and enumerates the concrete outputs (ingredient grams, yeast amount, fermentation schedule, warnings, calculator URL). It clearly distinguishes itself from sibling search/list/troubleshoot tools by describing its planning role.
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 gives an explicit pre-call workflow: call search_ovens and search_flours, gather temperatures, ask about preferment, and pair with troubleshoot if the user wants technique guidance. It also says when not to interrogate the user ('skip any the user already volunteered'), which is concrete usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the Pandough flour database. Returns flour names, protein %, W strength, hydration ranges, and types. Use this to help users find the right flour for their bake.
| Name | Required | Description | Default |
|---|---|---|---|
| tag | No | Filter by tag (e.g. 'pizza', 'bread', 'pastry') | |
| query | No | Search term (name, brand, or type) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already establishes this is a safe read tool, and the description adds the concrete return payload (names, protein %, W strength, hydration ranges, types). It does not disclose search semantics such as whether query and tag combine, whether results are filtered by default, or whether there is any result limit or pagination. With annotations covering safety, this is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences front-load the core action and return fields, then state the intended use case. There is no redundant wording or repetition of schema details, so every sentence 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?
For a simple read-only search tool with two optional parameters and no output schema, the description is mostly complete: it names the resource, the use case, and the returned fields. It could be more complete by explaining how tag and query interact or what happens when both are supplied, but the schema already documents each parameter individually.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema describes both parameters ('tag' and 'query') with clear descriptions, and schema description coverage is 100%, so the baseline is 3. The tool description adds no extra meaning about how these parameters behave or interact, so it does not go 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?
The description clearly identifies a specific verb and resource: 'Search the Pandough flour database.' It also enumerates the return fields (flour names, protein %, W strength, hydration ranges, types), which tells the agent what the tool produces. It does not explicitly contrast itself with the related get_flour_details sibling, so it falls just short of full differentiation.
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 a clear use context: 'Use this to help users find the right flour for their bake.' This helps an agent decide when to reach for this tool. However, it does not state when not to use it or mention alternatives like get_flour_details, so it lacks exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the Pandough oven database. Returns oven specs (max temperature, type, fuel) plus recommended bake settings — top/deck heat, bake time, and deck material — computed for a Neapolitan pizza by the same heat engine the site uses.
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | Oven type filter (e.g. 'pizza-oven', 'home-oven', 'outdoor') | |
| query | No | Search term (oven name, brand) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
ReadOnlyHint already covers safety, and the description adds meaningful behavioral context: it returns dynamically computed bake settings for a Neapolitan pizza rather than only static oven specs. It does not mention pagination or result limits, but for a read-only search this is acceptable.
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 with no filler: the search target is front-loaded, and the return contents are listed compactly. Every part adds useful 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?
For a search tool with two optional parameters and no output schema, the description adequately covers what is returned: oven specs plus computed bake settings. It stops short of describing behavior for empty queries or result ordering, but those are minor 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 schema already documents both 'type' and 'query'. The description does not add additional parameter-level detail, matching the baseline 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?
States a specific verb and resource: 'Search the Pandough oven database.' It clearly differentiates itself from sibling search_flours by focusing on ovens and describes the output domain (oven specs and bake settings).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives such as plan_bake or search_flours. The intended use is only implied by the tool name and general description, with no exclusions or routing hints.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the Pandough baking knowledge base for troubleshooting advice, technique guides, and flour science. Returns curated expert content about common baking issues.
| Name | Required | Description | Default |
|---|---|---|---|
| issue | Yes | The baking issue or question (e.g. 'sticky dough', 'no oven spring', 'cold proof technique') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, so the tool's safe read-only nature is covered. The description adds that it searches a 'knowledge base' and 'returns curated expert content,' but it does not describe search behavior, result formatting, or any limitations. Since the safety profile is already declared, this is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences and front-loads the core action and topic. The second sentence adds return-value context about curated expert content, though it partially restates the first sentence's 'troubleshooting advice.' Overall it is efficient and well-structured.
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 one-parameter, read-only search tool, the description is complete enough to guide invocation. It explains the knowledge base domain, the type of content returned, and the issue focus. Without an output schema, it does not detail response structure, but the tool is simple enough that this is not a critical 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?
The schema fully describes the single 'issue' parameter with an example, so parameter semantics are already covered. The description indirectly refers to the parameter by mentioning 'common baking issues' but adds no additional meaning beyond the schema. With 100% schema coverage, baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: 'Search the Pandough baking knowledge base for troubleshooting advice, technique guides, and flour science.' This clearly distinguishes it from siblings like search_flours and search_ovens by focusing on baking problems and expert content. The title 'Troubleshoot Dough' reinforces the purpose.
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 the tool should be used for baking issues or questions ('troubleshooting advice', 'common baking issues'), which is clear context. However, it does not explicitly state when not to use it or mention alternative tools such as search_flours for flour-specific lookups. Usage guidance is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user or an account that owns the GitHub organization, then choose Claim with GitHub.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!
Add one secure layer between your agents and this server.
Each tool targets a distinct domain object or action: flour lookup/detail, oven lookup, recipe style enumeration, bake planning, active bake listing, and troubleshooting. Even search_flours and get_flour_details are clearly separated into discovery vs. deep detail, so there is no real ambiguity.
Most tool names follow a clear verb_noun pattern such as search_flours, get_flour_details, list_recipes, and plan_bake. The bare verb 'troubleshoot' breaks the pattern slightly, but overall the naming is predictable and readable.
Seven tools is well-scoped for a baking planning domain and covers the core workflow from ingredient and equipment lookup to planning and troubleshooting. Each tool earns its place without unnecessary bloat or a too-thin surface.
The set covers the main bake-planning workflow well: find flour and oven, choose a recipe style, plan the bake, and get troubleshooting help. Minor gaps exist around managing active bakes beyond listing them and lack of a dedicated recipe detail endpoint, but agents can work around these.